Systems and methods for quantification of, and prediction of smoking behavior

ABSTRACT

Systems and methods for monitoring of biometric and contextual variables to assist in screening for, quantification of, and prediction of smoking behavior, and for assisting in smoking cessation are described.

RELATED APPLICATION

This is a continuation of U.S. patent application Ser. No. 16/385,952filed Apr. 16, 2019, which is a continuation of U.S. patent applicationSer. No. 15/092,475 filed Apr. 6, 2016, now U.S. Pat. No. 10,306,922,which claims benefit of priority to U.S. Provisional Application No.62/143,924 filed on Apr. 7, 2015, the entirety of each of which isincorporated by reference.

FIELD OF THE INVENTION

The present disclosure relates to systems and methods for monitoring ofbiometric and contextual variables to assist in screening for smokingcessation. The systems and methods may non-invasively detect smokingbehavior for a patient. The systems and methods may quantify and/orpredict smoking behavior of the patient. The systems and methods mayassist in smoking cessation. In some embodiments, the systems andmethods provide for screening a general population during medical anddental visits and other suitable health related appointments. In someembodiments, the systems and methods provide for initiating and settingup a quit program for a patient who smokes. In some embodiments, thesystems and methods provide for a follow up program after the patientsuccessfully quits smoking.

BACKGROUND

The health problems associated with tobacco smoking are well known.Cigarette smoke contains nicotine as well as many other chemicalcompounds and additives. Tobacco smoke exposes an individual to carbonmonoxide as well as these other compounds, many of which arecarcinogenic and toxic to the smoker and those around the smoker. Thepresence and level of carbon monoxide in the exhaled breath of thesmoker can provide a marker for identifying the overall smoking behaviorof that individual as well as provide a marker for their overallexposure to the other toxic compounds.

Because of the health risks and problems associated with smoking, inaddition to the effects of smoke on exposed non-smokers, many programsexist to assist an individual in cessation of smoking or at least reducethe amount of smoking on a daily basis.

Smoking cessation programs and products typically attempt to reduce thepatient's smoking without fully understanding the smoking behavior thatcan vary between patients. In addition, it may be difficult tounderstand a patient's smoking behavior given that self-reporting ofsmoking behavior relies on strict compliance with reporting smokingactivities. And in many cases, individuals may not strictly comply withreporting such activities due to shame, carelessness, and/or human errorassociated with tracking and assessing cigarette smoking.

There remains a need to address smoking in individuals by firstunderstanding the individual's smoking behavior and then, based on thisunderstanding, engage the individual with effective means for reducingand ultimately stopping smoking.

SUMMARY OF THE INVENTION

The system and methods described herein allow for a multi-phasedapproach to engaging individuals that smoke and quantifying theirsmoking behavior to better assist the individual to eventually achievethe goal of smoking cessation. The methods and systems described hereinallow for improved measuring and quantifying of a smoker's behaviorbefore that individual is even given the difficult task of attempting toquit smoking. For example, the systems and methods described herein areuseful to identify a population of smokers from within a largerpopulation using objective criteria. Once the individual smoker isidentified, the same methods and systems allow for a learn and explorephase where the individual's specific smoking behavior can be trackedand quantified. The methods and systems also allow for the individual'sbehavioral data to be tracked to identify potential triggers to smokingor simply to educate the individual on the extent of their smoking. Themethods and systems also allow for a more active monitoring of theindividual that has decided to engage in a “quit” program, where suchmonitoring allows the individual to self-monitor as well as monitoringby peers, coaches, or counselors. Lastly, the methods and systemsdisclosed herein can be used to monitor the individuals who successfullyquit smoking to ensure that smoking behavior does not re-occur.

Systems and methods for assessment of a smoking behavior of anindividual are described herein. The system can permit quantifying theindividual's smoking behavior by measuring biometric data and assessingfor factors attributable to cigarette smoke as well as assessingbehavioral data associated with smoking or with the individual'sordinary activity.

The systems and methods non-invasively can detect and quantify smokingbehavior for a patient based on measuring one or more of the patient'sbiometric data such as CO level or exhaled CO level. However otherbiometric data can also be used. Such data includes carboxyhemoglobin(SpCO), oxyhemoglobin (SpO2), heart rate, respiratory rate, bloodpressure, body temperature, sweating, heart rate variability, electricalrhythm, pulse velocity, galvanic skin response, pupil size, geographiclocation, environment, ambient temperature, stressors, life events, andother suitable parameters. Such measurements or data collection can usea portable measuring unit or a fixed measuring unit, either of whichcommunicates with one or more electronic devices for performing thequantification analysis. Alternatively, the analysis can be performed inthe portable/fixed unit. For example, the portable unit can be coupledto a keychain, to the individual's cigarette lighter, cell phone, orother item that will be with the individual on a regular basis.Alternatively, the portable unit can be a stand-alone unit or can beworn by the individual.

In one variation, the methods described herein permit quantifying anindividual's smoking behavior by obtaining a plurality of samples ofexhaled air from the individual over a period of time and recording acollection time associated with each sample of exhaled air; measuring anamount of exhaled carbon monoxide for each of the samples of exhaledair; compiling a dataset comprising the amount of exhaled carbonmonoxide and the collection time for each sample of exhaled air;quantifying an exposure of exhaled carbon monoxide over an interval oftime within the period of time and assigning an exhaled carbon monoxideload to the interval of time using the dataset; and displaying theexhaled carbon monoxide load. Displaying the quantified result can occurat one or more locations to provide feedback to the individual, acaregiver, or any other individual having a stake in understandingand/or reducing the individual's smoking behavior.

In an additional variation, obtaining the plurality of samples ofexhaled air from the individual over the period of time and recordingthe collection time of each sample of exhaled air comprises sequentiallyobtaining the plurality of sample of exhaled air.

Quantifying the exposure of exhaled carbon monoxide can comprisecorrelating a function of exhaled carbon monoxide versus time over theperiod of time using the dataset. Alternatively, the quantification cancomprise a mathematical product of the CO level and time over theinterval of time. Such quantification allows for an improved observationof the smoking behavior since it allows observation of the totalexposure of the body to CO over a given interval of time.

In an additional variation, the method further comprises obtaining anarea of exhaled carbon monoxide and time under a curve defined by thefunction over the interval of time.

The method and system can further include comprising generating asignal, e.g., using a portable device positioned with the individual, toremind the individual to provide at least one sample of exhaled air. Inadditional variations, the method can include alerting the individual ona repeating basis to provide the sample of exhaled air over the periodof time.

The method above can also include receiving an input data from theindividual and recording a time of the input. Such data can includebehavioral data such as a count of a portion of a cigarette smoked bythe individual. Alternatively, the data can include information onlocation (via a GPS unit), diet, activity (e.g., driving, watching TV,dining, working, socializing, etc.).

The method can include visually displaying any of the input data.Including a summation of the count of portion of the cigarettes smokedby the individual. Such data can also allow the display of calculatedinformation. For example, a cigarette count can be used to determine anindividual's nicotine exposure from cigarettes when a direct biologicalmeasurement might also erroneously measure nicotine from a nicotinepatch or nicotine gum. In addition, the cigarette data can be used toestimate a cost associated with the number of cigarettes smoked by theindividual and displaying such a cigarette cost.

The method can further comprise providing the visual display of theexhaled carbon monoxide load in association with a visual displayinterval of time within the period of time. Additionally, the method caninclude providing a visual display of a count of a number of theplurality of samples of exhaled air.

The method can also include determining a series of exhaled carbonmonoxide loads for a series of intervals of time within the period oftime. These values can be displayed in addition to the informationdiscussed herein.

The method can further include transmitting the amount of exhaled carbonmonoxide and the collection time associated with each sample of exhaledair from the portable sensor to an electronic device.

Another variation of quantifying an individual's smoking behavior caninclude obtaining a plurality of samples of carbon monoxide from theindividual over a period of time and recording a collection timeassociated with each sample of carbon monoxide; compiling a datasetcomprising the amount of carbon monoxide and the collection time foreach sample of carbon monoxide; quantifying an exposure of carbonmonoxide over an interval of time within the period of time andassigning a carbon monoxide load to the interval of time using thedataset; and displaying the carbon monoxide load. In such a case, theamount of carbon monoxide is determined from any type of measurementthat identifies carbon monoxide levels in the body.

The method can further comprise obtaining the plurality of samples ofcarbon monoxide comprises obtaining a plurality of samples of exhaledair from the individual over time and measuring an amount of exhaledcarbon monoxide for each of the samples of exhaled air and whererecording the collection time associated with each sample of carbonmonoxide comprises recording the collection time associated with eachsample of exhaled air.

The present disclosure also includes a device for obtaining data toquantify an individual's smoking behavior. Where such a device canassist or perform the functions described herein.

In one example, the device includes a portable breathing unit configuredto receive a plurality of samples of exhaled air from the individualover a period of time and configured to recording a collection timeassociated with each sample of exhaled air; a sensor located within theunit and configured to measure an amount of exhaled carbon monoxide foreach of the samples of exhaled air; at least one an input switchconfigured to record input data from the individual; a storage unitconfigured to store at least the amount of exhaled carbon monoxide andthe collection time; a transmitter configured to transmit the amount ofexhaled carbon monoxide, the collection time, and input data to anexternal electronic device; and an alarm unit configured to provide analarm to the user for submitting the plurality of samples of exhaledair.

The methods described herein can also include methods for preparing aprogram to assist in cessation of smoking for a patient who smokes. Forexample, the method can include measuring at least one biologicalindicator determinative of whether the patient smoked; capturing aplurality of patient data, where the patient data comprises informationcorrelated in time to when the patient smoked; combining at least one ofthe plurality of patient data and at least one biological indicator todetermine a smoking behavior model of the patient during a first testingperiod; assessing the smoking behavior model to assess a degree ofintervention required for a quit program; and providing a summary reportof the smoking behavior model and the degree of intervention.

In another variation, the present disclosure includes a system fordeterring a patient from smoking. For instance, the system can include asensor for measuring at least one biological indicator determinative ofwhether the patient smoked; a data collecting device configured tocapture a plurality of patient data, where the patient data comprisesinformation correlated in time to when the patient smoked; a processorin communication with the sensor and data collecting device, theprocessor configured to compile the plurality of patient data and atleast one biological indicator to determine a smoking behavior model ofthe patient over a first testing period; the processor configured togenerate at least one perturbation signal before a second testingperiod, and where the processor analyzes at least one of the patientdata captured over the second testing period to determine a testedsmoking behavior; and where the processor compares the tested smokingbehavior to the model smoking behavior to determine whether the patientwas deterred from smoking.

The present disclosure also includes methods for deterring a patientfrom smoking. For example, such a method can include measuring at leastone biological indicator determinative of whether the patient smoked;collecting a plurality of patient data, where the patient data comprisesinformation correlated in time to when the patient smoked; compiling theplurality of patient data with the at least one biological indicator todetermine a smoking behavior model of the patient; generating at leastone perturbation signal before a second testing period, where the atleast one perturbation signal affects the patient; analyzing at leastone of the patient data captured over the second testing period todetermine a tested smoking behavior; and comparing the tested smokingbehavior to the model smoking behavior to determine a change from thesmoking behavior model to determine whether the patient was deterredfrom smoking.

In another variation, a system for deterring a patient from smoking caninclude a database containing a smoking behavior model of the patient,where the smoking behavior model comprises a plurality of historicalpatient data correlated in time to when the patient smoked; a sensor formeasuring at least one biological indicator determinative of whether thepatient smoked; a processor configured to determine an expected smokingevent upon analyzing the smoking behavior model and upon determining theexpected smoking event the processor generates at least one perturbationsignal prior to a testing period; the processor configured to review theat least one biological indicator during the testing period to determinewhether the patient was deterred from smoking during the testing period;and where the processor updates the database containing the smokingbehavior model after determining whether or not the patient was deterredfrom smoking during the testing period.

Another variation of the method for deterring smoking can includeaccessing a database containing a smoking behavior model of the patient,where the smoking behavior model comprises a plurality of historicalpatient data correlated in time to when the patient smoked; estimatingan expected smoking event upon analyzing the smoking behavior model;generating at least one perturbation signal prior to a testing periodupon determining the expected smoking event the processor; measuring atleast one biological indicator determinative of whether the patientsmoked during the testing period; reviewing the at least one biologicalindicator during the testing period to determine whether the patient wasdeterred from smoking during the testing period; and updating thedatabase containing the smoking behavior model after determining whetheror not the patient was deterred from smoking during the testing period.

The above is a brief description of some the methods and systems toquantify a smoking behavior as well as programs for effective smokingcessation. Other features, advantages, and embodiments of the inventionwill be apparent to those skilled in the art from the followingdescription and accompanying drawings, wherein, for purposes ofillustration only, specific forms of the invention are set forth indetail. Variations of the access device and procedures described hereininclude combinations of features of the various embodiments orcombination of the embodiments themselves wherever possible.

Although the present disclosure discusses cigarettes in the variousexamples, the methods, systems and improvements disclosed herein can beapplied to any type of tobacco smoke or other inhaled type of smoke. Insuch cases, the disclosure contemplate the replacement of “cigarette”with the appropriate type of tobacco or smoke generating product(including, but not limited to, cigars, pipes, etc.)

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustrative system including a wearable device, amobile device, and a remote server in communication with the wearabledevice and the mobile device in accordance with some embodiments of thedisclosure;

FIG. 2 depicts another illustrative system including a wearable deviceand a remote server in communication with the wearable device inaccordance with some embodiments of the disclosure;

FIG. 3 depicts illustrative light absorption curves for various types ofhemoglobin, allowing for measurement of the levels of carboxyhemoglobin(SpCO) and oxyhemoglobin (SpO2) using a photoplethysmography (PPG)sensor in accordance with some embodiments of the disclosure;

FIG. 4 depicts a chart for a patient's varying levels of SpCO for atypical five-day monitoring period prior to commencing a smokingcessation program in accordance with some embodiments of the disclosure;

FIG. 5 depicts a trend of SpCO levels and smoking triggers for a patientover a typical day prior to commencing a smoking cessation program inaccordance with some embodiments of the disclosure;

FIG. 6 depicts a data structure for storing SpCO levels and smokingtriggers for a patient over a typical day in accordance with someembodiments of the disclosure;

FIG. 7 depicts an illustrative flow diagram for detecting smokingbehavior of a patient in accordance with some embodiments of thedisclosure;

FIG. 8 depicts a sample report after a five-day evaluation for a patientin accordance with some embodiments of the disclosure;

FIG. 9 depicts an illustrative chart of patient SpCO levels duringrun-in and quit program in accordance with some embodiments of thedisclosure;

FIG. 10 depicts an illustrative smart phone app screen showingmeasurements such as SpCO, SpO2, heart rate, respiratory rate, bloodpressure, and body temperature in accordance with some embodiments ofthe disclosure;

FIG. 11 depicts an illustrative smart phone app screen for receivingpatient entered data in accordance with some embodiments of thedisclosure;

FIG. 12 depicts an illustrative smart phone app screen implementing asmoking prevention protocol in accordance with some embodiments of thedisclosure;

FIG. 13 depicts an illustrative smart phone app screen for presentingthe quit process as a game for the patient in accordance with someembodiments of the disclosure;

FIG. 14 depicts an illustrative flow diagram for predicting andpreventing an expected smoking event in accordance with some embodimentsof the disclosure;

FIG. 15 depicts an illustrative flow diagram for step 1414 in FIG. 14for determining whether a prevention protocol was successful inaccordance with some embodiments of the disclosure;

FIG. 16 depicts an illustrative flow diagram for a one time measurementof the patient's SpCO level using a PPG sensor in accordance with someembodiments of the disclosure;

FIG. 17 depicts an illustrative flow diagram for detecting a smokingevent in accordance with some embodiments of the disclosure; and

FIG. 18 depicts an illustrative flow diagram for applying one or moreperturbations to a model for a patient's smoking behavior in accordancewith some embodiments of the disclosure.

FIG. 19 illustrates another variation of a system and/or method foraffecting an individual's smoking behavior using a number of the aspectsdescribed herein as well as further quantifying an exposure of theindividual to cigarette smoke.

FIG. 20A illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 19.

FIG. 20B illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 19.

FIG. 21 illustrates an example of a dataset used to determine the eCOcurve over a period of time where the eCO attributable to the smokingbehavior of the individual can be quantified over various intervals oftime to determine an eCO Burden or eCO Load for each interval.

FIG. 22 illustrates an example of displaying the biometric data as wellas various other information for assessing the smoking behavior of theindividual.

FIG. 23 shows another variation of a dashboard displaying similarinformation to that shown in FIG. 22.

FIGS. 24A to 24C illustrate another variation of a dataset comprisingexhaled carbon monoxide, collection time, and cigarette data quantifiedand displayed to benefit the individual attempting to understand theirsmoking behavior

DETAILED DESCRIPTION THE INVENTION

Systems and methods for smoking cessation with interactive screening aredescribed. The systems and methods non-invasively detect and quantifysmoking behavior for a patient based on measuring one or more of thepatient's carbon monoxide levels, exhaled carbon monoxide levels (eCO),carboxyhemoglobin (SpCO), oxyhemoglobin (SpO2), heart rate, respiratoryrate, blood pressure, body temperature, sweating, heart ratevariability, electrical rhythm, pulse velocity, galvanic skin response,pupil size, geographic location, environment, ambient temperature,stressors, life events, and other suitable parameters. It is noted thatSpCO and eCO are two ways to measure of the CO levels in the patient'sblood.

In some embodiments, the systems and methods described herein providefor screening a general population during medical and dental visits andother suitable health related appointments. A wearable device may beapplied to patients during, e.g., their annual visits, to detect recentsmoking behavior and, if positive, refer the smokers for further testingand ultimately to a smoking cessation program. In some embodiments, thewearable device is applied as a one time on-the-spot measurement. Insome embodiments, the patient is provided with a wearable device to wearas an outpatient for a period of time, e.g., one day, one week, oranother suitable period of time. Longer wear times may provide moresensitivity in detection of smoking behavior and more accuracy inquantifying the variables related to smoking behavior.

When wearing the wearable device for a suitable period of time, e.g.,five days, a number of parameters may be measured real-time or near realtime. These parameters may include, but are not limited to, CO, eCO,SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters. Data from the wearable device may be sent to asmart phone or a cloud server or another suitable device, either in realtime, near real time, at the end of each day, or according to anothersuitable time interval. The wearable device or the smart phone maymeasure parameters, including but not limited to, movement, location,time of day, patient entered data, and other suitable parameters. Thepatient entered data may include stressors, life events, location, dailyevents, administrations of nicotine patches or other nicotine formulas,administrations of other drugs for smoking cessation, and other suitablepatient entered data. For example, some of the patient entered data mayinclude information regarding phone calls, athletics, work, sport,stress, sex, drinking, smoking, and other suitable patient entered data.The received data may be compiled, analyzed for trends, and correlatedeither real time or after the period of time is complete.

From the parameters measured above, information regarding smoking may bederived via a processor located in the wearable device, the smart phone,the cloud server, or another suitable device. For example, the processormay analyze the information to determine CO, eCO, SpCO trends, averages,peaks, changes, specific curve signatures, slopes of change and othertypes of changes, and determine associations with other biometric andcontextual variable trends during day, and how those variables changebefore, during and after smoking. The processor may analyze the CO, eCO,SpCO trends to determine parameters such as total number of cigarettessmoked, average number of cigarettes smoked per day, maximum number ofcigarettes smoked per day, intensity of each cigarette smoked, quantityof each cigarette smoked, time to smoke each cigarette, time of day, dayof week, associated stressors, geography, location, and movement. Forexample, the total number of peaks in a given day may indicate thenumber of cigarettes smoked, while the shape and size and othercharacteristics of each peak may indicate the intensity and amount ofeach cigarette smoked. The processor may analyze heart rate and/or pulserate data to determine correlations between trends, averages, and peaksand patient smoking behavior. For example, the processor may correlatechanges in heart rate, such as tachycardia or heart rate variability,that occurs before or during a smoking event that can predict when apatient will smoke. This information may be used to preempt a smokingevent during a quit program. For example, if a smoking event ispredicted to occur within the next 10 minutes, the patient may benotified to deliver a dose of nicotine by any of a number of mechanismssuch as delivery via a transdermal patch or a transdermal transfer froma reservoir of nicotine stored in the wearable device.

In some embodiments, the systems and methods described herein providefor evaluating smoking behavior of a patient in two testing periods.During a first testing period, the patient behaves as they normallywould. The processor located in the wearable device, the smart phone,the cloud server, or another suitable device receives patient datarelating to the patient's smoking behavior. There is very little to noengagement of the patient as the purpose of the testing period is toobserve the patient's smoking patterns. Before the second testingperiod, the processor determines a model of how the patient smokes.

During a second testing period, the processor applies a series ofperturbations to the model to see if the smoking behavior changes. Theperturbations may be applied to the model using a machine learningprocess. The machine learning process delivers perturbations, tests theresults, and adjusts the perturbation accordingly. The processordetermines what works best to achieve an identified behavior change bytrying options via the machine learning process.

There may be several types of perturbations, each with severaldimensions. For example, the perturbation may be whether sending a textmessage before or during a smoking event causes the smoking event to beaverted or shortened. Dimensions within the perturbation may bedifferent senders, different timing, and/or different content for thetext messages. In another example, the perturbation may be whether aphone call at certain times of the day or before or during a smokingevent causes the smoking event to be averted or shortened. Dimensionswithin the perturbation may be different callers, different timing,and/or different content for the phone calls. In yet another example,the perturbation may be whether alerting the patient to review theirsmoking behavior at several points in the day averts smoking for aperiod of time thereafter. Dimensions may include determining whetherand when that aversion extinguishes. In other examples, theperturbations may be rewards, team play, or other suitable triggers toavert or shorten the patient's smoking events.

In some embodiments, the systems and methods described herein providefor initiating and setting up a quit program for a patient. After thepatient has completed a five-day evaluation while wearing the wearabledevice, the full dataset is compiled and analyzed by the system anddelivered to the patient or a doctor for the quit program. For example,a sample report may indicate that, from October 1 to October 6, Mr.Jones smoked a total of 175 cigarettes with an average number of 35cigarettes smoked per day, and a maximum number of 45 cigarettes smokedin one day. Mr. Jones' CO level averaged at 5.5% with a maximum of 20.7%and stayed above 4% for 60% of duration of the five-day evaluationperiod. Mr. Jones' triggers included work, home stressors, and commute.The report recommends a high dose and frequency nicotine levelprediction for commencing nicotine replacement therapy in view of Mr.Jones' smoking habits. This may enable a higher likelihood of patientcompliance in the quit program if the therapeutic regimen is customizedto the patient needs from the outset.

In some embodiments, the data collected during the five-day evaluation,while the patient is smoking as usual and prior to the quit program, isused to establish a baseline for a patient's vital signs, e.g., CO, eCO,SpCO level. The system may generate a baseline curve for the patient'svital signs based on variance in CO, eCO, SpCO levels in the collecteddata. The baseline curve may serve as a reference for comparing againstfuture measurements of the patient's CO, eCO, SpCO levels.

In some embodiments, the patient works with their doctor or counselor tobegin the process to enter the quit program. Having the objective datain front of the doctor and patient may assist in set up for the quitprogram and for setting drug and counseling plans. In some embodiments,the system sets up a quit program automatically based on the data fromthe evaluation period. The collected data may impact the quit programinitiation and set-up for the patient immediately before they enter theprogram by assisting in drug selection and dosing. For example,indication of higher and frequent smoking may prompt starting on highernicotine replacement therapy dose or multiple drugs (e.g., addingmedication used to treat nicotine addiction, such as varenicline).

The collected data may impact the quit program initiation and set-up bydetermining frequency, type, and duration of counseling required for thepatient. The data may lead to stratification of smoker needs. Forexample, highest risk smokers with highest use may get moreinterventions while lower risk smokers may get fewer interventions.Interventions may include a text message, a phone call, a socialnetworking message, or another suitable event, from the patient'sspouse, friend, doctor, or another suitable stakeholder, at certaintimes of days when the patient is likely to smoke.

The collected data may impact the quit program initiation and set-up bycorrelating smoking behavior with all variables above such as stressorsprompting smoking, time of day, and other suitable variables used forcounseling the patient up front to be aware of these triggers.Counseling interventions may target these stressors. Interventions maybe targeted at those times of day for the patient, such as a textmessage or phone call at those times of day. The collected data mayimpact the quit program initiation and set-up by assigning peer groupsbased on smoking behavior. The collected data may be used to predictand/or avert a smoking event. For example, if tachycardia or heart ratevariability precedes most smoking events, this may sound an alarm andthe patient may administer a dose of drug or can receive a phone callfrom a peer group, a doctor, or a counselor thereby averting a smokingevent.

In some embodiments, the systems and methods described herein providefor maintaining participation in the quit program for the patient. Oncein the quit program, the patient may continue to wear the wearabledevice for monitoring. The system may employ analytic tools such assetting an CO, eCO, SpCO baseline and tracking progress against thisbaseline. For example, the trend may drop to zero and stay there(indicating no more smoking). The trend may drop slowly with peaks andvalleys (indicating reduction in smoking). The trend may drop to zerothen spike for a recurrence (indicating a relapse).

The system may present the process for the patient as a game and improvevisibility of progress. For example, the system may provide the patientwith a small reward in exchange for abstaining from smoking for acertain period of time. In some embodiments, the system may transmit thedata in real time to a health care provider for remote monitoring andallowing the provider to efficiently monitor and adjust patient carewithout having the patient present in the office every day.

In some embodiments, the systems and methods described herein providefor a follow up program after a patient successfully quits smoking.After a successful quit, verified by the system, the patient wears thewearable device for an extended period of time, e.g., a few months totwo years, as an early detection system for relapse. The system maycollect data and employ counseling strategies as described above for thequit program.

In some embodiments, the systems and methods described herein providefor a system comprising one or more mobile devices and a server incommunication with the mobile devices. FIG. 1 shows an exemplaryembodiment 100 for such a system including device 102, device 104, andserver 106 in communication with devices 102 and 104. Device 102 assistsin detecting a patient's smoking behavior. Device 102 includes aprocessor, a memory, and a communications link for sending and receivingdata from device 104 and/or server 106. Device 102 includes one or moresensors to measure the patient's smoking behavior based on measuring oneor more of the patient's CO, eCO, SpCO₃ SpO2, heart rate, respiratoryrate, blood pressure, body temperature, sweating, heart ratevariability, electrical rhythm, pulse velocity, galvanic skin response,pupil size, geographic location, environment, ambient temperature,stressors, life events, and other suitable parameters. For example,device 102 may include PPG-based sensors for measuring CO, eCO, SpCO andSpO2, electrocardiography-based sensors for measuring heart rate andblood pressure, acoustic signal processing-based sensors for measuringrespiratory rate, wearable temperature sensors for measuring bodytemperature, electrodermal activity-based sensors for measuring skinconductance, electroencephalogram, implantable sensors placed in theskin, fat or muscle that measure CO and other variables, intra-oral COsensors, ambient CO sensors, and other suitable sensors. These sensorsmay have a variety of locations on or in the body for optimalmonitoring.

Device 102 may be carryable or wearable. For example, device 102 may bewearable in a manner similar to a wristwatch. In another example, device102 may be carryable or wearable and attached to the finger tip, earlobe, ear pinna, toe, chest, ankle, arm, a fold of skin, or anothersuitable body part. Device 102 may attach to the suitable body part viaclips, bands, straps, adhesively applied sensor pads, or anothersuitable medium. For example, device 102 may be attached to a finger tipvia a finger clip. In another example, device 102 may be attached to theear lobe or ear pinna via an ear clip. In yet another example, device102 may be attached to the toe via a toe clip. In yet another example,device 102 may be attached to the chest via a chest strap. In yetanother example, device 102 may be attached to the ankle via an ankleband. In yet another example, device 102 may be attached to the arm viabicep or tricep straps. In yet another example, device 102 may beattached to a fold of skin via sensor pads.

Device 102 may prompt the patient for a sample or the device, if worn,may take a sample without needing patient volition. The sampling may besporadic, continuous, near continuous, periodic, or based on any othersuitable interval. In some embodiments, the sampling is continuouslyperformed as often as the sensor is capable of making the measurement.In some embodiments, the sampling is performed continuously after a settime interval, such as five or fifteen minutes or another suitable timeinterval.

In some embodiments, device 102 includes one or more sensors to monitorSpCO using a transcutaneous method such as PPG. The transcutaneousmonitoring may employ transmissive or reflectance methods. Device 104may be a smart phone or another suitable mobile device. Device 104includes a processor, a memory, and a communications link for sendingand receiving data from device 102 and/or server 106. Device 104 mayreceive data from device 102. Device 104 may include an accelerometer, aglobal positioning system-based sensor, a gyroscopic sensor, and othersuitable sensors for tracking the described parameters. Device 104 maymeasure certain parameters, including but not limited to, movement,location, time of day, patient entered data, and other suitableparameters

The patient entered data received by device 104 may include stressors,life events, geographic location, daily events, administrations ofnicotine patches or other formulas, administrations of other drugs forsmoking cessation, and other suitable patient entered data. For example,some of the patient entered data may include information regarding phonecalls, athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. Patient use of their smart phone fortexting, calling, surfing, playing games, and other suitable use mayalso be correlated with smoking behavior, and these correlationsleveraged for predicting behavior and changing behavior. Device 104 orserver 106 (subsequent to receiving the data) may compile the data,analyze the data for trends, and correlate the data either real time orafter a specified period of time is complete. Server 106 includes aprocessor, a memory, and a communications link for sending and receivingdata from device 102 and/or device 104. Server 106 may be located remoteto devices 102 and 104 at, e.g., a healthcare provider site, or anothersuitable location.

FIG. 2 shows an exemplary embodiment 200 for a system including device202 and server 204 in communication with device 202. Device 202 assistsin detecting a patient's smoking behavior. Device 202 includes aprocessor, a memory, and a communications link for sending and receivingdata from server 204. Device 202 may be carryable or wearable. Forexample, device 202 may be wearable in a manner similar to a wristwatch.Device 202 includes one or more sensors 206 to measure the patient'ssmoking behavior based on measuring one or more of the patient's CO,eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters.

Device 202 may include one or more sensors 208 to measure certainparameters, including but not limited to, movement, location, time ofday, patient entered data, and other suitable parameters. The patiententered data may include stressors, life events, location, daily events,administrations of nicotine patches or other formulas, administrationsof other drugs for smoking cessation, and other suitable patient entereddata. The patient entered data may be received in response to a promptto the patient on, e.g., a mobile device such as device 104, or enteredwithout prompting on the patient's volition. For example, some of thepatient entered data may include information regarding phone calls,athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. Device 202 or server 204 (subsequent toreceiving the data) may compile the data, analyze the data for trends,and correlate the data either real time or after a specified period oftime is complete. Server 204 includes a processor, a memory, and acommunications link for sending and receiving data from device 202.Server 204 may be located remote to device 202 at, e.g., a healthcareprovider site, or another suitable location.

In some embodiments, device 102 or 202 includes a detector unit and acommunications unit. Device 102 or 202 may include a user interface asappropriate for its specific functions. The user interface may receiveinput via a touch screen, keyboard, or another suitable input mechanism.The detector unit includes at least one test element that is capable ofdetecting a substance using an input of a biological parameter from thepatient that is indicative of smoking behavior. The detector unitanalyzes the biological input from the patient, such as expired gas fromthe lungs, saliva, or wavelengths of light directed through or reflectedby tissue. In some embodiments, the detector unit monitors patient SpCOusing PPG. The detector unit may optionally measure a number of othervariables including, but not limited to, SpO2, heart rate, respiratoryrate, blood pressure, body temperature, sweating, heart ratevariability, electrical rhythm, pulse velocity, galvanic skin response,pupil size, geographic location, environment, ambient temperature,stressors, life events, and other suitable parameters. For breath-basedsensors, patient input may include blowing into a tube as part of thedetector unit. For saliva or other body fluid-based sensors, patientinput may include placement of a fluid sample in a test chamber providedin the detector unit.

For light-based sensors such as PPG, patient input may include placementof an emitter-detector on a finger or other area of exposed skin. Thedetector unit logs the date and time of day, quantifies the presence ofthe targeted substance, and stores the data for future analysis and/orsends the data to another location for analysis, e.g., device 104 orserver 106. The communications unit includes appropriate circuitry forestablishing a communications link with another device, e.g., device104, via a wired or wireless connection. The wireless connection may beestablished using WI-FI, BLUETOOTH, radio frequency, or another suitableprotocol.

FIG. 3 depicts an illustrative embodiment 300 of suitable wavelengthsfor analyzing SpO2 and SpCO using a light-based sensor. SpCO for apatient may be measured by intermittently testing the patient's exhaledbreath with a suitable sensor. In another example, SpCO for the patientmay be measured using a transcutaneous method such asphotoplethysmography (PPG). SpCO is detected by passing light throughpatient tissue, e.g., ear lobe, ear pinna, finger tip, toe, a fold ofskin, or another suitable body part, and analyzing attenuation ofvarious wavelengths. SpO2 is typically measured using two wavelengths,e.g., 302 (660 nm) and 306 (940 nm). SpCO may be measured using threewavelengths, e.g., 302 (660 nm), 304 (810 nm), and 306 (940 nm), or upto seven or more wavelengths, e.g., ranging from 500-1000 nm. Such a PPGsensor may be implemented via finger clips, bands, adhesively appliedsensor pads, or another suitable medium. The PPG sensor may betransmissive, such as used in many pulse oximeters. In transmissive PPGsensors, two or more waveforms of light are transmitted through patienttissue, e.g., a finger, and a sensor/receiver on the other side of thetarget analyzes the received waveforms to determine SpCO. Alternatively,the PPG sensor may be reflective. In reflectance PPG sensors, light isshined against the target, e.g., a finger, and the receiver/sensor picksup reflected light to determine the measurement of SpCO. More detailsare provided below.

Transcutaneous or transmucosal sensors are capable of non-invasivelydetermining blood CO level and other parameters based on analysis of theattenuation of light signals passing through tissue. Transmissivesensors are typically put against a thin body part, such as the earlobe, ear pinna, finger tip, toe, a fold of skin, or another suitablebody part. Light is shined from one side of the tissue and detected onthe other side. The light diodes on one side are tuned to a specific setof wavelengths. The receiver or detector on the other side detects whichwaveforms are transmitted and how much they are attenuated. Thisinformation is used to determine the percentage binding of O2 and/or COto hemoglobin molecules, i.e., SpO2 and/or SpCO.

Reflectance sensors may be used on a thicker body part, such as thewrist. The light that is shined at the surface is not measured at theother side but instead at the same side in the form of light reflectedfrom the surface. The wavelengths and attenuation of the reflected lightis used to determine SpO2 and/or SpCO. In some embodiments, issues dueto motion of the patient wrist are corrected using an accelerometer. Forexample, the information from the accelerometer is used to correcterrors in the SpO2 and SpCO values due to motion. Examples of suchsensors are disclosed in U.S. Pat. No. 8,224,411, entitled “NoninvasiveMulti-Parameter Patient Monitor.” Another example of a suitable sensoris disclosed in U.S. Pat. No. 8,311,601, entitled “Reflectance and/orTransmissive Pulse Oximeter”. These two U.S. Patents are incorporated byreference herein in their entireties, including all materialsincorporated by reference therein.

In some embodiments, device 102 or 202 is configured to recognize aunique characteristic of the patient, such as a fingerprint, retinalscan, voice label or other biometric identifier, in order to preventhaving a surrogate respond to the signaling and test prompts to defeatthe system. For this purpose, a patient identification sub-unit may beincluded in device 102 or 202. Persons of ordinary skill in the art mayconfigure the identification sub-unit as needed to include one or moreof a fingerprint scanner, retinal scanner, voice analyzer, or facerecognition as are known in the art. Examples of suitable identificationsub-units are disclosed, for example in U.S. Pat. No. 7,716,383,entitled “Flash-interfaced Fingerprint Sensor,” and U.S. PatentApplication Publication No. 2007/0005988, entitled “MultimodalAuthentication,” each of which is incorporated by reference herein intheir entirety.

The identification sub-unit may include a built in still or video camerafor recording a picture or video of the patient automatically as thebiological input is provided to the test element. Regardless of the typeof identification protocol used, device 102 or 202 may associate theidentification with the specific biological input, for example by timereference, and may store that information along with other informationregarding that specific biological input for later analysis.

A patient may also attempt to defeat the detector by blowing into thedetector with a pump, bladder, billows, or other device, for example,when testing exhaled breath. In the embodiment of saliva testing, apatient may attempt to substitute a clean liquid such as water. Forlight based sensors, the patient may ask a friend to stand in for him orher. Means to defeat these attempts may be incorporated in to thesystem. For example, device 102 or 202 may incorporate the capability ofdiscerning between real and simulated breath delivery. Thisfunctionality may be incorporated by configuring the detector unit tosense oxygen and carbon dioxide, as well as the target substance (e.g.,carbon monoxide). In this manner, the detector unit can confirm that thegas being analyzed is coming from expired breath having lower oxygen andhigher carbon dioxide than ambient air. In another example, the detectorunit may be configured to detect enzymes naturally occurring in salivaso as to distinguish between saliva and other liquids. In yet anotherexample, light based sensors may be used to measure blood chemistryparameters other than CO level and thus results may be compared to knownsamples representing the patient's blood chemistry.

In some embodiments, device 104 (e.g., a smart phone) receivesmeasurements from device 102 (e.g., a wearable device) in real time,near real time, or periodically according to a suitable interval. Device104 may provide a user interface for prompting a patient for certaininputs. Device 104 may provide a user interface for displaying certainoutputs of the collected data. Device 104 may permit the patient toinput information that the patient believes relevant to his or hercondition without prompting or in response to prompting. Suchinformation may include information about the patient's state of mindsuch as feeling stressed or anxious. Such unprompted information may becorrelated to a biological input based on a predetermined algorithm,such as being associated with the biological input that is closest intime to the unprompted input, or associated with the first biologicalinput occurring after the unprompted input. Server 106 (e.g., ahealthcare database server) may receive such data from one or both ofdevices 102 and 104. In some embodiments, the data may be stored on acombination of one or more of devices 102, 104, and 106. The data may bereported to various stakeholders, such as the patient, patient's doctor,peer groups, family, counselors, employer, and other suitablestakeholders.

In some embodiments, a wearable device, e.g., device 102 or 202, may beapplied to patients during, e.g., their usual annual visits, to detectsmoking behavior and then refer the smokers to quit programs. Thepatient is provided with a wearable device to wear as an outpatient fora period of time, e.g., one day, one week, or another suitable period oftime. Longer wear times may provide more sensitivity in detection ofsmoking behavior and more accuracy in quantifying the variables relatedto smoking behavior. FIG. 7 below provides an illustrative flow diagramfor detecting smoking behavior and will be described in more detailbelow.

In some embodiments, employers ask employees to voluntarily wear thewearable device for a period of time, such as one day, one week, oranother suitable period of time. The incentive program may be similar toprograms for biometric screening for obesity, hyperlipidemia, diabetes,hypertension, and other suitable health conditions. In some embodiments,health care insurance companies ask their subscribers to wear thewearable device for a suitable period of time to detect smokingbehavior. Based on the smoking behavior being quantified, these patientsmay be referred to a smoking cessation program as described in thepresent disclosure.

When wearing the wearable device for a suitable period of time, e.g.,five days, a number of parameters may be measured real-time or near realtime. These parameters may include, but are not limited to, CO, eCO,SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters. FIG. 4 shows illustrative chart 400 for a patient'svarying levels of SpCO for a typical five-day monitoring period. Datapoints 402 and 404 indicate high level of CO which in turn likelyindicates high smoking events. Data points 406 and 408 indicate lowlevel of CO likely because the patient was asleep or otherwise occupied.One or more algorithms may be applied to the granular data points on thecurve to detect a smoking event with adequate sensitivity andspecificity. For example, the algorithms may analyze one or more ofshape of the SpCO curve, start point, upstroke, slope, peak, delta,downslope, upslope, time of change, area under curve, and other suitablefactors, to detect the smoking event.

Data from the wearable device may be sent to a smart phone, e.g., device104, or a cloud server, e.g., server 106 or 204, either in real time, atthe end of each day, or according to another suitable time interval. Thesmart phone may measure parameters, including but not limited to,movement, location, time of day, patient entered data, and othersuitable parameters. The patient entered data may include stressors,life events, location, daily events, administrations of nicotine patchesor other formulas, administrations of other drugs for smoking cessation,and other suitable patient entered data. For example, some of thepatient entered data may include information regarding phone calls,athletics, work, sport, stress, sex, drinking, smoking, and othersuitable patient entered data. The received data may be compiled,analyzed for trends, and correlated either real time or after the periodof time is complete.

From the parameters measured above, information regarding smoking may bederived via a processor located in, e.g., device 102, 104, or 202, orserver 106 or 204. For example, the processor may analyze theinformation to determine CO trends, averages, peaks, and associations,other vital sign trends during day, and how do those vitals changebefore, during and after smoking. FIG. 5 shows an illustrative diagram500 for the analyzed information. The patient may arrive at FIG. 5 byzooming in on a given day in FIG. 4. Data point 502 indicates the SpCOlevel when the patient is asleep. Data point 504 shows that when thepatient wakes up, the SpCO level is the lowest. Data points 506, 508,and 510 indicate high SpCO levels are associated with triggers such aswork breaks, lunch, and commute. The processor may analyze the SpCOtrends in FIG. 5 to determine parameters such as total number ofcigarettes smoked, average number of cigarettes smoked per day, maximumnumber of cigarettes smoked per day, intensity of each cigarette smoked,quantity of each cigarette smoked, what that patient's smoking eventlooks like on the curve to be used later for quit program, time of day,day of week, associated stressors, geography, location, and movement.For example, the total number of peaks in a given day may indicate thenumber of cigarettes smoked, while the gradient of each peak mayindicate the intensity of each cigarette smoked.

FIG. 6 depicts an illustrative data structure for storing patient data.In this embodiment, data structure 600 illustrates patient data 602associated with data points in FIG. 5, e.g., data point 508. Patientdata 602 includes identifying information for the patient such aspatient name 604 and patient age 606. Patient data 602 includes curvedata 608 corresponding to the curve in FIG. 5. For example, curve data608 includes curve identifier 610 corresponding to data point 508. Thedata corresponding to data point 508 may be collected by device 102,104, or 202, and/or server 106 or 204 or a combination thereof. Dataassociated with curve identifier 610 includes day, time, and locationinformation 612. The data includes patient vital signs such as CO and O2levels 614. The data includes patient entered data such as trigger 616.The patient entered data may be entered in response to a prompt to thepatient on, e.g., device 104, or entered without prompting on thepatient's volition. Curve data 608 includes curve identifier 618 foradditional data points in FIG. 5. Data structure 600 may be adapted asappropriate for storing patient data.

FIG. 7 depicts an illustrative flow diagram 700 for detecting smokingbehavior of a patient over a suitable evaluation period. When thepatient wears the wearable device for a suitable period of time, e.g.,five days, a number of parameters may be measured in real-time, nearreal time, at the end of each day, or according to another suitable timeinterval. These parameters may include, but are not limited to, CO, eCO,SpCO, SpO2, heart rate, respiratory rate, blood pressure, bodytemperature, sweating, heart rate variability, electrical rhythm, pulsevelocity, galvanic skin response, pupil size, geographic location,environment, ambient temperature, stressors, life events, and othersuitable parameters. The wearable device or another suitable device maymeasure parameters, including but not limited to, movement, location,time of day, and other suitable parameters.

At step 702, a processor in a smart phone, e.g., device 104, or a cloudserver, e.g., server 106 or 204, receives the described patient data. Atstep 704, the processor receives patient entered data in response to aprompt displayed to the patient on, e.g., a smart phone, and/or patientdata entered without a prompt on the patient's volition. The patiententered data may include stressors, life events, location, daily events,administrations of nicotine patches or other formulas, administrationsof other drugs for smoking cessation, and other suitable patient entereddata. At step 706, the processor sends instructions to update a patientdatabase with the received data. For example, the processor may transmitthe patient data to a healthcare provider server or a cloud server thathosts the patient database.

At step 708, the processor analyzes the patient data to determinesmoking events. The processor may compile the data, analyze the data fortrends, and correlate the data either real time or after the evaluationperiod is complete. For example, the processor may analyze theinformation to determine CO trends, averages, peaks, shape of curve, andassociations, other vital sign trends during day, and how those vitalschange before during and after smoking. The processor may analyze theSpCO trends to determine parameters such as total number of cigarettessmoked, average number of cigarettes smoked per day, maximum number ofcigarettes smoked per day, intensity of each cigarette smoked, time ofday, day of week, associated stressors, geography, location, andmovement. For example, the total number of peaks in a given day mayindicate the number of cigarettes smoked, while the gradient of eachpeak may indicate the intensity of each cigarette smoked.

At step 710, the processor transmits the determined smoking events andrelated analysis to the patient database for storage. At step 712, theprocessor determines whether the evaluation period has ended. Forexample, the evaluation period may be five days or another suitable timeperiod. If the evaluation period has not ended, the processor returns tostep 702 to receive additional patient data, analyze the data, andupdate the patient database accordingly.

If the evaluation period has ended, at step 714, the processor ends thedata collection and analysis. For example, the processor may evaluateall collected data at the end of the evaluation period to prepare areport as described with respect to FIG. 8 below.

It is contemplated that the steps or descriptions of FIG. 7 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 7 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 7.

In some embodiments, the systems and methods described herein providefor initiating and setting up a quit program for a patient. After thepatient has completed a five-day evaluation while wearing the wearabledevice, e.g., device 102 or 202, the full dataset is compiled andanalyzed by the system and delivered to the patient or a doctor for thequit program. FIG. 8 shows an illustrative embodiment 800 of a samplereport from the analysis. For example, the report indicates that, fromOctober 1 to October 6, Mr. Jones smoked a total of 175 cigarettes withan average number of 35 cigarettes smoked per day, and a maximum numberof 45 cigarettes smoked in one day. Mr. Jones' CO level averaged at 5.5%with a maximum of 20.7% and stayed above 4% for 60% of duration of thefive-day evaluation period. Mr. Jones' triggers included work, homestressors, and commute. The report recommends a high dose and frequencynicotine level prediction for commencing nicotine replacement therapy inview of Mr. Jones' smoking habits.

In some embodiments, the patient works with their doctor or counselor tobegin the process to enter the quit program. In some embodiments, thesystem sets up a quit program automatically based on the data from theevaluation period. The sample report in FIG. 8 is one example ofmeasuring SpCO and producing a report on CO exposure, associatedstressors, and predicting a starting nicotine dose requirement. Forexample, a high volume and intensity smoker may be more nicotinedependent at quit program entry, which the processor can estimate basedon five-day behavior, and the quit program would start the patient on ahigher nicotine replacement therapy dose. This may avoid many patientsfailing early in a quit program due to withdrawal symptoms. Based on thereport data, including average and maximum number of cigarettes smoked,SpCO levels, triggers, the processor may determine the dosage fornicotine for administration to the patient. For example, the processormay determine a high dosage of nicotine for patients that on averagesmoke more than a threshold number of cigarettes per day. As the reportdata is updated, the processor may update the dosage for nicotine aswell.

The collected data may impact the quit program initiation and set-up forthe patient immediately before they enter the program by assisting indrug selection and dosing. For example, indication of higher smoking mayprompt starting on higher nicotine replacement therapy dose or multipledrugs (e.g., adding medication used to treat nicotine addiction, such asvarenicline). The collected data may impact the quit program initiationand set-up by determining frequency, type, and duration of counselingrequired for the patient. The data may lead to stratification of smokerneeds. For example, highest risk smokers with highest use may get moreinterventions while lower risk smokers may get fewer interventions. Forexample, interventions may include a text message, a phone call, asocial networking message, or another suitable event, from the patient'sspouse, friend, doctor, or another suitable stakeholder, at certaintimes of days when the patient is likely to smoke.

The collected data may impact the quit program initiation and set-up bycorrelating smoking behavior with all variables above such as stressorsprompting smoking, time of day, and other suitable variables used forcounseling the patient up front to be aware of these triggers.Counseling interventions may target these stressors and there may beinterventions aimed at those times of day for the patient, such as atext message or call at those times of days. The collected data mayimpact the quit program initiation and set-up by assigning peer groupsbased on smoking behavior. The collected data may be used to predictand/or avert a smoking event. For example, if tachycardia or heart ratevariability or a suitable set of variables precedes most smoking events,this will sound an alarm and the patient may administer a dose of drugor can receive a call from a peer group, doctor, or counselor. FIG. 12shows an illustrative embodiment of preempting a smoking event and willbe discussed in more detail below. FIGS. 14 and 15 show illustrativeflow diagrams for predicting and preventing an expected smoking eventand will be discussed in more detail below.

In some embodiments, the systems and methods described herein providefor maintaining participation in the quit program for the patient. Oncein the quit program, the patient may continue to wear the wearabledevice, e.g., device 102 or 202, for monitoring. The system may employanalytic tools such as setting an SpCO baseline and tracking progressagainst this baseline. The trend may drop to zero and stay there(indicating no more smoking). The trend may drop slowly with peaks andvalleys (indicating reduction in smoking). The trend may drop to zerothen spike for a recurrence (indicating a relapse).

The system may employ patient engagement strategies by providing smallinfrequent rewards for group or individual progress to engage thepatient. The system may provide employer rewards, payers, spouse, orpeer groups to engage the patient. The system may present the processfor the patient as a game and improve visibility of progress. FIG. 13provides an illustrative embodiment of such a user interface and will bediscussed in more detail below. In some embodiments, the system maytransmit the data in real time to a health care provider for remotemonitoring and allowing the provider to efficiently monitor and adjustpatient care without having to have them in the office every day. Forexample, the provider may send instructions to the system to adjustmedication type and dose, alter intensity of counseling, call and textfor positively encouraging progress, or trigger an intervention if thepatient is failing to refrain from smoking. This may supplant staffedquit phone lines which are expensive and may efficiently automate theprocess. The system may employ increased intensity and frequency toimprove outcomes in patients. The system may encourage the patient viasupport from spouse, employer, health care provider, peers, friends, andother suitable parties via scheduled phone calls, text messages, orother suitable communications.

FIG. 9 shows an illustrative graph 900 for tracking the average dailySpCO trend for a patient running up to and then entering a quit program.The average trend is tracked for each day as it improves. The doctor orcounselor may zoom in on a particular day (present or past) to see thegranular detail and associations of CO with other parameters measuredand associated stressors 910. Visibility of the trend of CO over time inthe quit program may prevent patient dropouts, prevent smoking relapse,titrate drugs and counseling, and improve outcomes. For example datapoint 902 indicates CO level before the patient entered the quitprogram. Data points 904 and 906 indicate CO levels as nicotinereplacement therapy and varenicline therapy is administered during thequit program. Data point 908 indicates the patient has successfully quitsmoking. At this point, the system may recommend the patient enter intoa recidivism prevention program to prevent relapse.

In some embodiments, the systems and methods described herein providefor a follow up program after a patient successfully quits smoking.After a successful quit, verified by the system, the patient wears thewearable device, e.g., device 102 or 202, for an extended period oftime, e.g., a few months to two years, as an early detection system forrelapse. The system may collect data and employ counseling strategies asdescribed above for the quit program.

In some embodiments, a patient receives a wearable device, e.g., device102 or 202, and an app for their smartphone, e.g., device 104, thatallows them to assess health remotely and privately by tracking severaldifferent parameters. The patient may submit breath samples or put theirfinger in or on a sensor on the wearable device several times per day asrequired. They may wear the wearable device to get more frequent or evencontinuous measurements. At the end of a test period, e.g., five toseven days or another suitable time period, the processor in the smartphone may calculate their CO exposure and related parameters. FIG. 10shows an exemplary app screen 1000 showing measurements such as SpCO1002, SpO2 1004, heart rate 1006, respiratory rate 1008, blood pressure1010, and body temperature 1012. Warning indicators 1014 and 1016 may beprovided for atypical measurements, possibly indicating effects ofsmoking on the body. The system may prompt the patient with alerts whenwarning indicators 1014 or 1016 are activated.

The system may recommend the patient enter a smoking cessation programand provide options for such programs. The patient may agree to enter aquit program on seeing such objective evidence of smoking. The systemmay share this data with the patient's spouse, their doctor, or anothersuitable stakeholder involved in the patient's quit program. Forexample, the system may share the data with an application astakeholder's mobile device or send a message including the data viaemail, phone, social networking, or another suitable medium. Triggers toget a patient to join the quit program may include spousal suggestion,employer incentive, peer pressure, personal choice, an illness, oranother suitable trigger. The patient may initiate the quit program ontheir own or bring the data to a doctor to receive assistance in joininga quit program.

While the patient is initiated in the quit program, the wearable device,e.g., device 102 or 202, may continue to monitor the patient's healthparameters, such as heart rate, movement, location that precede thesmoking behavior, and transmit the data to the patient and/or his doctorto improve therapy. The smart phone app on, e.g., device 104, mayreceive patient entered data including, but not limited to, stressors,life events, location, daily events, administrations of nicotine patchesor other formulas, administrations of other drugs for smoking cessation,and other suitable patient entered data.

FIG. 11 shows an illustrative embodiment of an app screen 1100 forreceiving patient entered data. App screen 1100 may be displayed whenthe smart phone app receives an indication of a smoking event, e.g., dueto a spike in the CO level for the patient. App screen 1100 prompts theuser to enter a trigger for the smoking event. For example, the patientmay select from one of options 1102, 1104, 1106, and 1108 as triggeringa smoking event or select option 1110 and provide further informationregarding the trigger. Other triggers for a smoking event may includephone calls, athletics, sport, stress, sex, and other suitable patiententered data. The patient may voluntarily invoke app screen 1100 as wellto enter trigger information for a smoking event. In some embodiments,app screen 1100 for receiving patient data is displayed to the patientduring the five-day evaluation period to collect information regardingsmoking behavior before the patient enters the quit program.

In some embodiments, the collected data is used by the smart phone appto avert a smoking event. The processor running the app or a processorin another device, such as device 102 or 202 or server 106 or 204, mayanalyze the information regarding what happens to heart rate and othervital signs in the period leading up to a smoking event. The processormay correlate changes in heart rate, such as tachycardia, that canpredict when a patient will smoke. This information may be used toinitiate a prevention protocol for stopping the smoking event. Forexample, the prevention protocol may include delivering a bolus ofnicotine. The nicotine may be delivered via a transdermal patch or atransdermal transfer from a reservoir of nicotine stored in the wearabledevice, e.g., device 102 or 202. In another example, the preventionprotocol may include calling the patient's doctor, a peer group, oranother suitable stakeholder. The processor may send an instruction toan automated call system, e.g., resident at server 106 or 204, toinitiate the call. FIGS. 14 and 15 provide flow diagrams for predictinga smoking event based on patient vital signs and will be described inmore detail below.

FIG. 12 shows an illustrative embodiment of an app screen 1200implementing such a prevention protocol. For example, if a patient tendsto become tachycardic twenty minutes before every cigarette, theprocessor may detect tachycardia and prompt the patient to administernicotine via option 1202. The patient may vary the nicotine dose viaoption 1204. In some embodiments, the nicotine is administeredautomatically. The amount may be determined based on the patient'scurrent SpCO level or another suitable parameter. The patient mayreceive a call from a peer group via option 1206, a doctor via 1208, oranother suitable stakeholder. The caller may provide the patientencouragement to abstain from smoking and suggest seeking out otheractivities to divert the patient's attention.

In some embodiments, the smart phone app presents the process for thepatient as a game to improve visibility of progress. The app may employpatient engagement strategies by providing small frequent or infrequentrewards for group or individual progress to engage the patient. The appmay provide employer rewards, payers, spouse, or peer groups to engagethe patient. FIG. 13 shows an illustrative app screen 1300 for such anembodiment. App screen 1300 offers the patient a reward for abstainingfrom smoking for fifteen days. Prompt 1302 challenges the patient tofurther abstain for another fifteen days. The patient may select option1304 to accept the reward and continue monitoring progress while heremains smoke free. However, the patient may be having difficultyabstaining and may select option 1306 to be contacted a peer group, acounselor, a family member, a doctor, or another suitable party.

In some embodiments, the patient is a peer and supporter for others intheir group. Groups can track each other's progress and give support.For example, the group members may be part of a social network thatallows them to view each other's statistics and provide encouragement toabstain from smoking. In another example, a message, e.g., a tweet, maybe sent to group members of the patient's social network, e.g.,followers, when it is detected the patient is smoking. The message mayinform the group members that the patient needs help. The group mayconnect to the patient in a variety of ways to offer help. Thisinteraction may enable to the patient to further abstain from smokingthat day.

In some embodiments, at a primary care visit a patient provides a sampleand is asked if they smoke. For example, the wearable device, e.g.,device 102 or 202, is applied to the patient and receives the sample fora one time on-the-spot measurement of the patient's SpCO level. The SpCOlevel may exceed a certain threshold which suggests that the patientsmokes. FIG. 16 provides a flow diagram for the one time measurement ofthe patient's SpCO level. The patient may be provided with the wearabledevice to wear as an outpatient for a period of time, e.g., one day, oneweek, or another suitable period of time. Longer wear times may providemore sensitivity in detection of smoking behavior and more accuracy inquantifying the variables related to smoking behavior.

The wearable device, e.g., device 102 or 202, and smart phone app on,e.g., device 104, may continue to monitor the patient's healthparameters, such as SpCO level, in real time or near real time andprocess the data for observation by the patient, the doctor, or anyother suitable party. The smart phone app may also offer the data in adigestible form for daily or weekly consumption by the patient and/orthe doctor. For example, the smart phone app may generate displaysimilar to FIG. 9 showing daily progress with the option to zoom into aparticular day for observing further details. The doctor may log thepatient into a healthcare database stored at, e.g., server 106 or 204 incommunication with a mobile device running the smart phone app, andcontinue to receive the data from the smart phone app via the Internetor another suitable communications link. The smart phone app may receivedata from the sensors via a wired connection to the mobile devicerunning the app or via a wireless connection such as WI-FI, BLUETOOTH,radio frequency, or another suitable communications link.

The patient and the doctor may set a future quit date and send thepatient home without any drugs or with drugs to help the patient quit.The patient may begin working towards the agreed quit date. Feedbackfrom the wearable device and/or the smart phone app may assist thepatient to be more prepared at the quit date to actually quit as well asto smoke less at the quit date than when they started at the start. Oncethe patient starts the quit program, they may get daily or weeklyfeedback from their spouse, doctor, nurse, counselor, peers, friends, orany other suitable party.

Drug therapy, if prescribed, may be based by the doctor or may beadjusted automatically based on patient performance. For example, thedoctor may remotely increase or decrease nicotine dose administrationbased on the patient's CO, eCO, SpCO level. In another example, aprocessor in the wearable device, e.g., device 102 or 202, the smartphone, e.g., device 104, or a remote server, e.g., server 106 or 204,may increase or decrease nicotine dose administration based on CO trendsfrom the patient's past measurements. Similarly, the drug therapy may beshortened or lengthened in duration according to collected data.

FIG. 14 depicts an illustrative flow diagram 1400 for predicting asmoking event based on a patient's CO, eCO, SpCO measurements and othersuitable factors. The patient may be given a wearable device, e.g.,device 102 or 202, and a smart phone app for their mobile phone, e.g.,device 104. The wearable device may include a PPG sensor for measuringthe patient's SpCO level. At step 1402, a processor in the wearabledevice or the patient's mobile phone receives a PPG measurement for thepatient's SpCO level and associated time and location. The processor mayalso receive other information such as heart rate, respiration rate, andother suitable factors in predicting a smoking event.

At step 1404, the processor updates a patient database that is storedlocally or at a remote location, such as a healthcare database in server106, with the received patient data.

At step 1406, the processor analyzes the current and prior measurementsfor the patient parameters and determines whether a smoking event isexpected. For example, the SpCO trend may be at a local minimum whichindicates the user may be reaching for a cigarette to raise their SpCOlevel. The processor may apply a gradient descent algorithm to determinethe local minimum. At step 1408, the processor determines whether theSpCO trend indicates an expected smoking event. If the processordetermines a smoking event is not expected, at step 1410, the processordetermines if the time and/or location are indicative of an expectedsmoking event. For example, the processor may determine that the patienttypically smokes when they wake up in the morning around 7 a.m. Inanother example, the processor may determine that the patient typicallysmokes soon after they arrive at work. In yet another example, theprocessor may determine that the patient typically smokes in the eveningwhenever they visit a particular restaurant or bar.

If the processor determines a smoking event is expected from eithersteps 1408 or 1410, at step 1412, the processor initiates a preventionprotocol for the patient to prevent the smoking event. Informationregarding the prevention protocol may be stored in memory of device 102,104, or 202, or server 106 or 204, or a combination thereof. Theinformation for the prevention protocol may include instructions for oneor more intervention options to initiate when the patient is about tosmoke. For example, the processor may initiate an alarm in the patient'smobile phone and display an app screen similar to FIG. 12. The appscreen may offer the patient options to administer nicotine or receive acall from a peer group, a doctor, or another suitable party.Alternatively, the prevention protocol may include automaticallyadministering a bolus of nicotine to the patient from a reservoir ofnicotine stored in the patient's wearable device. In another example,the app screen may indicate that a message, e.g., a tweet, will be sentto group members of the patient's social network, e.g., followers, whenit is detected the patient has failed to abstain from smoking. Thepatient may refrain from smoking to prevent the message indicating hisfailure from being sent out.

In some embodiments, steps 1408 and 1410 are combined into one step orinclude two or more steps for a processor determining that a smokingevent is expected. For example, the processor may determine that asmoking event is expected based on a combination of the SpCO trend, thepatient's location, and/or the current time. In another example, theprocessor may determine that a smoking event is expected based on aseries of steps for analyzing one or more of the patient's SpCO, SpO2,heart rate, respiratory rate, blood pressure, body temperature,sweating, heart rate variability, electrical rhythm, pulse velocity,galvanic skin response, pupil size, geographic location, environment,ambient temperature, stressors, life events, and other suitableparameters.

At step 1414, the processor determines whether the prevention protocolwas successful. If a smoking event occurred, at step 1418, the processorupdates the patient database to indicate that the prevention protocolwas not successful. If a smoking event did not occur, at step 1416, theprocessor updates the patient database to indicate that the preventionprotocol was successful. The processor returns to step 1402 to continuereceiving the PPG measurement for the patient's SpCO level andassociated data. The processor may monitor the patient's vital signscontinuously to ensure that the patient does not relapse into a smokingevent.

It is contemplated that the steps or descriptions of FIG. 14 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 14 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 14.

FIG. 15 depicts an illustrative flow diagram 1500 for determiningwhether the prevention protocol was successful in relation to step 1414in FIG. 14. At step 1502, the processor receives patient data fordetermining whether a smoking event occurred. At step 1504, theprocessor analyzes currently received patient data and previouslyreceived patient data. At step 1506, the processor determines whether asmoking event occurred based on the analysis. For example, if nonicotine was administered but the patient's SpCO levels are currentlyhigher than previous SpCO levels, the processor may determine thepatient relapsed and smoked a cigarette. In such a situation, at step1508, the processor returns a message indicating that the preventionprotocol was not successful. In another example, if the patient's vitalsigns indicate no rise or a drop in SpCO levels, the processor maydetermine that a smoking event did not occur. In such a situation, atstep 1510, the processor returns a message indicating that theprevention protocol was successful.

It is contemplated that the steps or descriptions of FIG. 15 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 15 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 15.

FIG. 16 depicts an illustrative flow diagram 1600 for a one timemeasurement of the patient's SpCO level using a PPG sensor. For example,the wearable device, e.g., device 102 or 202, is applied to the patientand receives the sample for a one time measurement of the patient's SpCOlevel. At step 1602, a processor in the wearable device receives a PPGmeasurement for the patient's SpCO level and any other suitable data,such as time, location, SpO2, heart rate, respiratory rate, bloodpressure, body temperature, sweating, heart rate variability, electricalrhythm, pulse velocity, galvanic skin response, pupil size, geographiclocation, environment, ambient temperature, stressors, life events, andother suitable parameters. At step 1604, the processor analyzes thereceived data to determine a recent smoking event. For example, anelevated SpCO level beyond a certain threshold may suggest that thepatient has recently smoked a cigarette.

At step 1606, the processor determines whether the patient SpCO levelindicates a smoking event has occurred. For example, the SpCO levelexceeding a specified threshold may indicate a smoking event. In anotherexample, one or more of shape of the SpCO curve, start point, upstroke,slope, peak, delta, downslope, upslope, time of change, area undercurve, and other suitable factors, may indicate a smoking event. One ormore of these factors may assist in quantification of the smoking event.For example, the total number of peaks in a given day may indicate thenumber of cigarettes smoked, while the gradient shape and size and othercharacteristics of each peak may indicate the intensity and amount ofeach cigarette smoked. If the processor determines the SpCO level isindicative that a smoking event has not occurred, at step 1608, theprocessor returns a denial message indicating the patient did not have arecent smoking event. The patient's doctor may find this informationuseful in evaluating the patient's smoking behavior. If the processordetermines the SpCO level is indicative that a smoking event hasoccurred, at step 1610, the processor returns a confirmation messageindicating the patient did have a recent smoking event. In this case,the collected data may be used to set up a quit program for the patientas described above.

After steps 1608 or 1610, at step 1612, the processor updates thepatient database to record this information. At step 1614, the processorterminates the SpCO level evaluation for the patient. The patient may beprovided with the wearable device to wear as an outpatient for a periodof time, e.g., one day, one week, or another suitable period of time.Longer wear times may provide more sensitivity in detection of smokingbehavior and more accuracy in quantifying the variables related tosmoking behavior.

It is contemplated that the steps or descriptions of FIG. 16 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 16 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 16.

In some embodiments, data from one or more devices associated withpatients, such as devices 102 and 104 or device 202, are received at acentral location, such as server 106 or 204. The patient devices log inreal time or near real time multiple biometric and contextual variables.For example, the biometric variables may include CO, eCO, SpCO, SpO2,heart rate, respiratory rate, blood pressure, body temperature,sweating, heart rate variability, electrical rhythm, pulse velocity,galvanic skin response, pupil size, and other suitable biometricvariables. For example, the contextual variables may include GPSlocation, patient activities (e.g., sports, gym, shopping, or anothersuitable patient activity), patient environment (e.g., at work, at home,in a car, in a bar, or another suitable patient environment), stressors,life events, and other suitable contextual variables. The collected datamay also include in-person observation of the patients' smokingbehavior. A spouse or friend or buddy may be able to enter data thattheir patient smoked, correlating that data with the SpCO readings todetermine accuracy.

Server 106 includes a processor for receiving data for multiple patientsover a period of time and analyzes the data for trends that occur aroundthe time of an actual smoking event. Based on the trends, the processordetermines a diagnostic and/or detection test for a smoking event. Thetest may include one or more algorithms applied to the data asdetermined by the processor. For example, the processor may analyze aspike in CO level of a patient. Detecting the spike may includedetermining that the CO level is above a certain specified level.Detecting the spike may include detecting a relative increase in thepatient's CO level from a previously measured baseline. The processormay detect a spike as a change in the slope of the patient's CO trendover a period of time. For example, the CO trend moving from a negativeslope to a positive slope may indicate a spike in the CO level. Inanother example, the processor may apply one or more algorithms tochanges in heart rate, increasing heart rate variability, changes inblood pressure, or variation in other suitable data in order to detect asmoking event.

FIG. 17 depicts an illustrative flow diagram 1700 for detecting asmoking event as described above. A processor (e.g., in server 106 or204) may determine a diagnostic and/or detection test for a smokingevent according to flow diagram 1700. At step 1702, the processorreceives current patient data. At step 1704, the processor retrievespreviously stored data for the patient from a database, e.g., a patientdatabase stored at server 106 or 204. At step 1706, the processorcompares the current and prior patient data to detect a smoking event.For example, the processor may analyze a spike in CO level of thepatient. Detecting the spike may include detecting a relative increasein the patient's CO level from a previously measured baseline. Theprocessor may detect a spike as a change in the slope of the patient'sCO trend over a period of time. For example, the CO trend moving from anegative slope to a positive slope may indicate smoking behavior. Inanother example, the processor may apply one or more algorithms tochanges in heart rate, increasing heart rate variability, changes inblood pressure, or variation in other suitable data in order to detect asmoking event. At step 1708, the processor determines whether a smokingevent occurred based on, e.g., a spike in CO level of the patient asdescribed. If no smoking event is detected, at step 1710, the processorreturns a message indicating that a smoking event did not occur. If asmoking event is detected, at step 1712, the processor returns a messageindicating that a smoking event occurred. At step 1714, the processorupdates the patient database with the results from either step 1710 or1712.

It is contemplated that the steps or descriptions of FIG. 17 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 17 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 17.

In some embodiments, the processor analyzes initially received data tomeasure when a person smokes and ties the algorithm to a variable thattriggers the algorithm for diagnosing and/or detecting a smoking event.The processor continues to analyze other variables as additional patientdata is received. The processor may determine another variable thatchanges when the patient smokes and instead use that variable to triggerthe algorithm. For example, the processor may opt to use the othervariable because it is less invasive or easier to measure than theinitially selected variable.

In some embodiments, the algorithm for detecting a smoking event has ahigh sensitivity. Sensitivity is defined as a percentage of the numberactual smoking events detected by the sensor and algorithm. For example,if a patient smokes 20 times in one day, and the algorithm identifiesevery smoking event, it is 100% sensitive.

In some embodiments, the algorithm for detecting a smoking event has ahigh specificity. Specificity is defined as the ability of the test tonot make false positive calls of a smoking event (i.e., positive testwith no smoking event present). If the sensor and algorithm do not makeany false positive calls in a day, it has 100% specificity.

In another example, if a patient smokes 20 times and the algorithmidentifies 18 of the 20 actual smoking events and indicates 20 otherfalse smoking events, it has 90% sensitivity (i.e., detected 90% ofsmoking events) and 50% specificity (i.e., over called the number ofsmoking events by 2×).

In some embodiments, after the processor determines one or morealgorithms and applies to SpCO measurements to detect a smoking eventwith adequate sensitivity and specificity, the processor determineswhether there is an association of other biometric variables orcontextual variables with the SpCO results that could be used on theirown (without SpCO) to detect a smoking event. The processor maydetermine another variable that changes when the patient smokes andinstead use that variable to trigger the algorithm. For example, theprocessor may opt to use the other variable because it is less invasiveor easier to measure or more reliable than the initially selectedvariable.

In some embodiments, the processor analyzes received patient data topredict a smoking event likelihood before it happens. The processor mayanalyze received patient data over a period of time, e.g., five minutes,10 minutes, 15 minutes, 20 minutes, or another suitable time interval,before a smoking event to determine one or more triggers. For example,some smoking events may be preceded by contextual triggers (e.g., at abar, before, during, or after eating, before, during, or after sex, oranother suitable contextual trigger). In another example, some smokingevents may be preceded by changes in a biometric variable, e.g., heartrate or another suitable biometric variable. The determined variablesmay overlap with those selected for diagnosis and detection andtherefore may be used for prediction as well. Alternatively, thedetermined variables may not overlap with those selected for diagnosis.

The processor may inform patients of a smoking event likelihood andtrigger a prevention protocol (e.g., as discussed with respect to FIGS.14 and 15) to prevent smoking change behavior. The processor detectssmoking events for a patient entered in, e.g., a quit program, andtracks and analyses trends in received patient data. The processor maydetermine goals for the patient and reward them when he achieves the setgoals (e.g., as discussed with respect to FIG. 13). The processor maypredict when a patient is about to smoke and intervene just in time bysuggesting a call to a peer group or a physician or by administering abolus of nicotine (e.g., as discussed with respect to FIG. 12).

In one example, the processor predicts smoking events for the patientbased on 75% of the patient's smoking events, during diagnosis, beingpreceded by increased heart rate (or a suitable change in anothervariable). During the quit program, the processor may apply one or morealgorithms to received patient data to predict smoking events andinitiate a prevention protocol. For example, the prevention protocol mayengage the patient just in time by putting the patient in contact withsupporters, such as a doctor, a counselor, a peer, a team member, anurse, a spouse, a friend, a robot, or another suitable supporter. Insome embodiments, the processor applies algorithms to adjust settings,such as baseline, thresholds, sensitivity, and other suitable settings,for each patient based on their five-day run-in diagnostic period. Theprocessor may then use these customized algorithms for the specificpatient's quit program. The described combination of techniques to altersmoking behavior in a patient may be referred to as a digital drug.

In some embodiments, the processor detects smoking in a binary mannerwith a positive or a negative indication. The processor initially usesobservational studies and SpCO measurements from the patient to detectsmoking behavior. For example, the processor receives data regardingtrue positives for smoking events from observational data for thepatient's smoking behavior. The processor determines if detection basedon SpCO measurements matches true positives for smoking events. If thereis a match, the processor applies algorithms to other received patientdata including patient's SpCO, SpO2, heart rate, respiratory rate, bloodpressure, body temperature, sweating, heart rate variability, electricalrhythm, pulse velocity, galvanic skin response, pupil size, geographiclocation, environment, ambient temperature, stressors, life events, andother suitable parameters. The processor determines whether any patternsin non-SpCO variable data are also indicative of a smoking event. Suchvariables may be used in algorithms for non-SpCO devices, such aswearable smart watches or heart rate monitor straps or other devices, todetect smoking events.

The processor may quantify smoking behavior when it is detected based onthe received patient data. For example, the processor analyzes SpCO datatrends to indicate how intensely the patient smoked each cigarette, howmany cigarettes the patient smoked in one day, how much of eachcigarette was smoked, and/or how long it took to smoke each cigarette.The processor may use other biometric or contextual variables for theindications as well. The processor uses the received patient data topredict the likelihood for a smoking event to occur in the near future,e.g., in the next 10 minutes. The processor may analyze the receivedpatient data over a preceding period of time, e.g., five minutes, 10minutes, 15 minutes, 20 minutes, or another suitable time interval,before a smoking event to determine one or more triggers.

In some embodiments, the systems and methods described herein providefor evaluating smoking behavior of a patient. During a five-day testingperiod, the patient behaves as they normally would. Devices 102, 104,and/or 106 or devices 202 and/or server 204 receive patient datarelating to the patient's smoking behavior. There is very little to noengagement of the patient as the purpose of the testing period is toobserve the patient's smoking patterns. The testing period may beextended to a second five-day period if needed. Alternatively, the firstand second periods may be shorter, e.g., two or three days, or longer,e.g., a week or more. Before the second testing period, the processordetermines a model of how the patient smokes.

In the second testing phase, the processor applies a series ofperturbations to the model to see if the smoking behavior changes. Theremay be several types of perturbations, each with several dimensions. Forexample, the perturbation may be whether sending a text message beforeor during a smoking event causes the smoking event to be averted orshortened. Dimensions within the perturbation may be different senders,different timing, and/or different content for the text messages. Inanother example, the perturbation may be whether a phone call at certaintimes of the day or before or during a smoking event causes the smokingevent to be averted or shortened. Dimensions within the perturbation maybe different callers, different timing, and/or different content for thephone calls. In yet another example, the perturbation may be whetheralerting the patient to review their smoking behavior at several pointsin the day averts smoking for a period of time thereafter. Dimensionsmay include determining whether and when that aversion extinguishes. Inother examples, the perturbations may be rewards, team play, or othersuitable triggers to avert or shorten the patient's smoking events.

In some embodiments, the processor delivers perturbations to the smokingmodel for the patient using a machine learning process. The machinelearning process delivers perturbations, tests the results, and adjuststhe perturbation accordingly. The processor determines what works bestto achieve an identified behavior change by trying options via themachine learning process. The machine learning process may be appliedduring the second testing phase as slight perturbations. The machinelearning process may be also be applied with significant perturbationsduring the patient's quit phase to increase efforts to try to get thepatient to quit smoking or to continue to abstain from smoking.

FIG. 18 depicts an illustrative flow diagram 1800 for applying one ormore perturbations to the smoking model for the patient in the secondtesting phase. At step 1802, a processor in wearable device 102 or 202,mobile device 104, or server 106 or 204 receives patient data relatingto the patient's smoking behavior in the first testing phase. At step1804, the processor analyzes the received patient data to determine amodel for the patient's smoking behavior. At step 1806, the processorapplies one or more perturbations to the model to see if the smokingbehavior changes. The perturbation may be applied to the model using amachine learning process. There may be several types of perturbations,each with several dimensions. For example, the perturbation may bewhether sending a text message before or during a smoking event causesthe smoking event to be averted or shortened. Dimensions within theperturbation may be different senders, different timing, and/ordifferent content for the text messages.

At step 1808, the processor determines whether the perturbation alteredthe patient's smoking behavior. For example, the processor determineswhether receiving a text message before or during a smoking event causedthe patient to abstain from or shorten his smoking. If the perturbationcaused a change in the patient's smoking behavior, at step 1810, theprocessor updates the model for the patient's smoking behavior toreflect the positive result of the applied perturbation. The processorthen proceeds to step 1812. Otherwise, the processor proceeds directlyto step 1812 from step 1808 and determines whether to apply anotherperturbation or a variation in the dimensions of the presentperturbation. The processor may use the machine learning process todetermine whether to apply additional perturbations to the model. If nomore perturbations need to be applied, at step 1814, the processor endsthe process of applying perturbations.

If more perturbations need to be applied, at step 1816, the processordetermines another perturbation to apply to the model. For example, theprocessor may adjust the present perturbation to send a text message tothe patient at a different time or with different content. In anotherexample, the processor may apply a different perturbation by initiatinga phone call to the patient before or during a smoking event. Theprocessor returns to step 1806 to apply the perturbation to the model.The processor may use the machine learning process to deliver aperturbation, test the result, and adjust the perturbation or selectedanother perturbation accordingly. In this manner, the processordetermines what works best to achieve an identified behavior change forthe patient by trying different options via the machine learningprocess.

It is contemplated that the steps or descriptions of FIG. 18 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 18 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order asappropriate or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation to FIG.1 (e.g., device 102, 104, or 106) or FIG. 2 (e.g., device 202 or 204)could be used to perform one or more of the steps in FIG. 18.

In an illustrative example, a 52-year old male patient is incentivizedby his employer to get screened for smoking behavior. The patient entersan evaluation program on Jun. 1, 2015. The patient reports smoking 20cigarettes per day. The program coordinator, such as a physician orcounselor, loads an app on the patient's smart phone, e.g., mobiledevice 104, and gives the patient a connected sensor, e.g., wearabledevice 102 or 202. The coordinator informs the patient to smoke andbehave normally for a five-day testing period and respond to promptsfrom the app as they arise. After the five-day period is over, thecoordinator enters the patient into a supplementary testing period wherethe app prompts a bit more often (e.g., to apply perturbations). Thecoordinator informs the patient that it is up to him at that point torespond however he wishes. The coordinator establishes a targeted dateof Jun. 10, 2015 to include 10 days of testing.

After the five-day testing period, the coordinator receives a report(e.g., a five-day report card as discussed with respect to FIG. 8). Thereport indicates 150 cigarette smoking events detected using CO ascompared to 100 cigarette smoking events based on the patient'sestimate. The report indicates that associated contextual variablesinclude alcohol, location, stress, and other suitable data. The reportindicates that associated biometric variables include increased heartrate, without exercise, as preceding 50% of smoking events. The reportindicates that prompts for stress levels showed increased stress in 20%of smoking events.

During the supplementary five-day test period, a processor in the mobiledevice, e.g., device 104, the wearable device, e.g., device 102 or 202,or a remote server, e.g., server 106 or 204, applies perturbations via amachine learning process. For example, the mobile device prompts thepatient four time times a day with a display including number ofcigarettes smoked, intensity of smoking, and time of day. As the dayprogresses, the prompts cause the patient to reduce smoking for longerperiods of time. The net effect is that the patient smokes fewercigarettes in the second half of day as compared to the first half. Inanother example, the mobile device prompts the patient at 10am everydaywith a display including number of cigarettes smoked the previous day.The net effect is studied as to how the prompt impacts the patient'ssmoking behavior for the rest of day. The machine learning process mayadjust time and content of the display to alter the dimensions of theperturbation as required.

In another example, the processor applies a perturbation via a machinelearning process in the form of a text message sent to the patientduring a smoking event. The machine learning process varies thedimensions of the perturbation by having different senders, differenttiming, sending before or during smoking, different content of message,different images in the message, and/or different rewards forabstaining. In another example, the processor applies a perturbation viaa machine learning process in the form of a phone call to the patientduring a smoking event. The machine learning process varies thedimensions of the perturbation by having different callers, differenttiming, calling before or during smoking, different content of call,different tones in the call, and/or different rewards for abstaining.

In another example, the processor applies a perturbation via a machinelearning process in the form of a prompt for a particular activity onthe patient's mobile device. The prompt indicates that the patient issmoking but should consider smoking only half a cigarette and then getoutside. During long times between cigarette events, or when an event ispredicted, the machine learning process applies perturbations to attemptto avert the smoking event completely. For example, the mobile devicedisplays a prompt notifying the patient that they are in a high riskzone and should consider an alternative activity or location or phone afriend.

After the testing period, the coordinator enters the patient into thequit program. During the quit period, the processor receives patientdata and applies algorithms to the data as described. The processor usesall data from the first and second testing periods to customize thealgorithms and starting regimen and quit program interventions for thespecific patient. The diagnostic and detection algorithms may use one ormore biometric variables for the patient, such as SpCO, to detectsmoking behavior. The quit program includes a nicotine regimen startingon day one as part of the nicotine replacement therapy. The nicotine maybe delivered via a transdermal patch or a transdermal transfer from areservoir of nicotine stored in the wearable device given to thepatient. The processor applies algorithms to the received patient datato determine the most effective interventions. The processor applies theinterventions and further adjusts them as required. The processor mayset goal event counts and determine which method works best for alteringthe patient's smoking behavior. The processor may invoke multiplepersonalized interventions from stakeholders as perturbations via themachine learning process and test which works best for altering thepatient's smoking behavior. The perturbations with the most impact onthe patient's smoking model may be retained, while those with less or noimpact may not be used further.

While exemplary embodiments of the systems and methods described abovefocus on smoking behaviors, examples of which include but are notlimited to smoking of tobacco via cigarettes, pipes, cigars, and waterpipes, and smoking of illegal products such as marijuana, cocaine,heroin, and alcohol related behaviors, it will be immediately apparentto those skilled in the art that the teachings of the present inventionare equally applicable to any number of other undesired behaviors. Suchother examples include: oral placement of certain substances, withspecific examples including but not limited to placing chewing tobaccoand snuff in the oral cavity, transdermal absorption of certainsubstances, with specific examples including but not limited toapplication on the skin of certain creams, ointments, gels, patches orother products that contain drugs of abuse, such as narcotics, and LSD,and nasal sniffing of drugs or substances of abuse, which includes butis not limited to sniffing cocaine.

In general, the basic configuration of devices 102 and 104 or device202, as well as related steps and methods as disclosed herein will besimilar as between the different behaviors that are being addressed. Thedevices may differ somewhat in design to account for different targetsubstances that are required for testing or different testingmethodology necessitated by the different markers associated withparticular undesired behaviors.

It will also be appreciated by persons of ordinary skill in the art thata patient participating in a formal cessation program may take advantageof the systems and methods disclosed herein as adjuncts to the cessationprogram. It will be equally appreciated that the patient may beindependently self-motivated and thus beneficially utilize the systemsand methods for quitting the undesired behavior unilaterally, outside ofa formal cessation program.

In further exemplary embodiments, the systems and methods disclosedherein may be readily adapted to data collection and in particular tocollection of reliable and verifiable data for studies related toundesired behaviors for which the present invention is well suited totest. Such studies may be accomplished with virtually no modification tothe underlying device or methods except that where treatment was notincluded there would not necessarily be a need for updating of the testprotocol or treatment protocol based on user inputs.

FIG. 19 illustrates another variation of a system and/or method foraffecting an individual's smoking behavior using a number of the aspectsdescribed herein as well as further quantifying an exposure of theindividual to cigarette smoke. In the illustrated example, a pluralityof samples of biometric data are obtained from the individual andanalyzed to quantify the individual's exposure to cigarette smoke suchthat the quantified information can be relayed to the individual, amedical caregiver, and/or other parties having a stake in theindividual's health. The example discussed below employs a portabledevice 1900 that obtains a plurality of samples of exhaled air from theindividual with commonly available sensors that measure an amount ofcarbon monoxide within the sample of exhaled air (also referred to asexhaled carbon monoxide or ECO). However, the quantification andinformation transfer is not limited to exposure of smoking based onexhaled air. As noted above, there are many sampling means to obtain anindividual's smoking exposure. The methods and devices described in thepresent example can be combined or supplemented with such sampling meanswhere possible while still remaining with the scope of the invention. Inaddition, while the present example discusses the use of a portablesampling unit, the methods and procedures described herein can be usedwith a dedicated or non-portable sampling unit.

The measurement of exhaled CO level has been known to serve as animmediate, non-invasive method of assessing a smoking status of anindividual. See for example, The Measurement of Exhaled Carbon Monoxidein Healthy Smokers and Non-smokers, S. Erhan Devecia, et al., Departmentof Public Health, Medical Faculty of Firat University, Elazig, Turkey2003 and Comparison of Tests Used to Distinguish Smokers fromNonsmokers, M. J. Jarvis et al. American Journal of Public Health,November 1987, V77, No. 11. These articles discuss that exhaled CO(“eCO”) levels for non-smokers can range between 3.61 ppm and 5.6 ppm.In one example, the cutoff level for eCO was above 8-10 ppm to identifya smoker.

Turning back to FIG. 19, as shown a portable or personal sampling unit1900 communicates with either a personal electronic device 110 or acomputer 112. Where the personal electronic device 110 includes, but isnot limited to a smart phone, ordinary phone, cellular phone, or otherpersonal transmitting device exclusively designed for receiving datafrom the personal sampling unit 1900). Likewise, the computer 112 isintended to include a personal computer, local server, or a remoteserver. Data transmission 114 from the personal sampling unit 1900 canoccur to both or either the personal electronic device 110 and/or thecomputer 112. Furthermore, synchronization 116 between the personalelectronic device 110 and the computer 112 is optional. Either thepersonal electronic device 110, the computer 112, and/or the personalsampling unit 1900 can transmit data to a remote server for dataanalysis as described herein. Alternatively, data analysis can occur,fully or partially, in a local device (such as the computer or personalelectronic device). In any case, the personal electronic device 100and/or computer 112 can provide information to the individual,caretaker, or other individual as shown in FIG. 19. I

In the depicted example of FIG. 19, the personal sampling unit 1900receives a sample of exhaled air 108 from the individual via acollection tube 1902 . Hardware within the personal sampling unit 1900includes any commercially available electrochemical gas sensor thatdetects carbon monoxide (CO) gas within the breath sample, commerciallyavailable transmission hardware that transmits data 114 (e.g., viaBluetooth, cellular, or other radio waves to provide transmission ofdata). The transmitted data and associated measurements andquantification are then displayed on either (or both) a computer display112 or a personal electronic device 110. Alternatively, or incombination, any of the information can be selectively displayed on theportable sampling unit 1900.

The personal sampling unit (or personal breathing unit) can also employstandard ports to allow direct-wired communication with the respectivedevices 110 and 112. In certain variations, the personal sampling unit1900 can also include memory storage, either detachable or built-in,such the memory permits recording of data and separate transmission ofdata. Alternatively, the personal sampling unit can allow simultaneousstorage and transmission of data. Additional variations of the device1900 do not require memory storage.

In addition, the unit 1900 can employ any number of GPS components,inertial sensors (to track movement), and/or other sensors that provideadditional information regarding the patient's behavior.

The personal sampling unit 1900 can also include any number of inputtrigger (such as a switch or sensors) 1904, 1906. As described below,the input trigger 1904, 1906 allow the individual to prime the device1900 for delivery of a breath sample 108 or to record other informationregarding the cigarette such as quantity of cigarette smoked, theintensity, etc. In addition, variations of the personal sampling unit1900 also associate a timestamp of any inputs to the device 1900. Forexample, the personal sampling unit 1900 can associate the time at whichthe sample is provided and provide the measured or inputted data alongwith the time of the measurement when transmitting data 114.Alternatively, the personal sampling device 1900 can use alternate meansto identify the time that the sample is obtained. For example, given aseries of samples rather than recording a timestamp for each sample, thetime periods between each of the samples in the series can be recorded.Therefore, identification of a timestamp of any one sample allowsdetermination of the time stamp for each of the samples in the series.

In certain variations, the personal sampling unit 1900 is designed suchthat it has a minimal profile and can be easily carried by theindividual with minimal effort. Therefore the input triggers 1904 cancomprise low profile tactile switches, optical switches, capacitivetouch switches, or any commonly used switch or sensor. The portablesampling unit 1900 can also provide feedback or information to the userusing any number of commonly known techniques. For example, as shown,the portable sampling unit 1900 can include a screen 1908 that showsselect information as discussed below. Alternatively or in addition, thefeedback can be in the form of a vibrational element, an audibleelement, and a visual element (e.g., an illumination source of one ormore colors). Any of the feedback components can be configured toprovide an alarm to the individual, which can serve as a reminder toprovide a sample and/or to provide feedback related to the measurementof smoking behavior. In addition, the feedback components can provide analert to the individual on a repeating basis in an effort to remind theindividual to provide periodic samples of exhaled air to extend theperiod of time for which the system captures biometric (such as eCO, COlevels, etc.) and other behavioral data (such as location either enteredmanually or via a GPS component coupled to the unit, number ofcigarettes, or other triggers). In certain cases, the reminders can betriggered at higher frequency during the initial program or datacapture. Once sufficient data is obtained, the reminder frequency can bereduced.

FIG. 20A illustrates a visual representation of data that can becollected with variations of the system shown in FIG. 19. As discussedabove, an individual provides breath samples using the portable samplingunit. The individual can be reminded at a regular interval or at randomintervals depending upon the nature of the treatment or interventionprogram. Each sample is evaluated by one or more sensors within theportable sampling unit to measure an amount of CO. The CO measurementstypically correspond to the inflection points 410 on the graph of FIG.20A. Each CO measurement 410 corresponds to a timestamp as shown in thehorizontal axis. The data accumulated via the portable sampling unitallows for the collection of a dataset comprising at least the COmeasurement and time of the sample which can be graphed to obtain an eCOcurve which is indicative of the amount of CO attributable to thesmoking behavior of the individual over the course of the time period.

As noted herein, the individual can further track additional informationsuch as smoking of a cigarette. The smoking of the cigarette can beassociated with its own time stamp as shown by bar 414. In one variationof the method and system under the present disclosure, the individualcan use the input triggers on the portable sampling unit to enter thenumber of cigarettes smoked or a fraction thereof. For example, eachactuation of the input trigger can be associated with a fractionalamount of a cigarette (e.g., ½, ⅓, ¼, etc).

FIG. 20B illustrates a portion of a graphic representation of datacollected as described above. However, in this variation, thequantification of an individual's smoking behavior can use behavioraldata to better approximate the CO value between eCO readings. Forexample, in some variations, eCO measurements between any two points 410can be approximated using a linear approximation between the two points.However, it is known that, in the absence of being exposed to new CO,the CO level decay within the bloodstream. This decay can beapproximated using a standard rate, a rate based on the biometricinformation of the patient (weight, heartbeat, activity, etc.) As shownin FIG. 20B, when the patient is between cigarettes 414, the calculatedCO level can follow a decay rate 440. Once the individual records acigarette 414, the CO increase 442 can again be approximated, either byusing a standard rate or one that is calculated using biometric data asdiscussed above, or based on the intensity, duration, and amount ofcigarettes smoked. Accordingly, the methods and system described hereincan optionally use an improved (or approximated) eCO curve 438 using thebehavioral data discussed above. Such an improved eCO rate can also beused to determine an improved eCO curve 438 while the individual sleeps.This improved eCO curve can then provide an improved eCO load asdescribed herein. The biometric information used to determine decay ratecan be measured by the portable sampling device or by external biometricmeasuring devices that communicate with the system.

This approximated or improved eCO curve 438 can be displayed to theindividual (or to a third party) as a means to help change behavior asthe individual can view a real time approximated CO level (i.e., therate of decrease when not smoking and the rate of increase whensmoking). Additional information can also be displayed, for example, thesystem can also calculate the amount of CO increase with each cigarettebased on their starting CO value.

FIG. 21 illustrates an example of a dataset used to determine the eCOcurve 412 over a period of time where the eCO attributable to thesmoking behavior of the individual can be quantified over variousintervals of time to determine an eCO Burden or eCO Load for eachinterval. As shown, the period of time extends along the horizontal axisand comprises historical and ongoing data captured/transmitted by theportable sampling unit. In order to provide more effective feedback tothe individual regarding their smoking behavior, the eCO curve 412during a certain time interval can be quantified. In the illustratedexample, the interval of time between times 416 and 418 comprises a 24hour interval of time. A subsequent 24 hour interval is defined betweentimes 418 and 420. The interval of time or time interval can compriseany time between two points within the period of time spanned by thedataset. In most cases, the interval of time will be compared to otherintervals of time having the same time duration (i.e., where eachinterval can comprise M minutes, H hours, D days, etc.).

One way of quantifying the eCO Burden/Load over the interval of time isto obtain the area defined by or underneath the eCO curve 412 between agiven interval of time (e.g., 416 to 418, 418 to 420, etc.) using thedataset as shown in the graph of FIG. 21. In the illustrated example,the eCO Burden/Load 422 for the first interval (416 to 418) comprises 41(measured in COppm * t), while the eCO Burden 422 for the secondinterval (418 to 420) comprises 37. As noted above, along with the eCOBurden/Load 422, the dataset can include the number of cigarettes smoked414 along with the timestamp of each cigarette. This cigarette data canalso be summarized 426 along with the eCO Burden/Load 422 for any giveninterval of time. In the illustrated example, the eCO Burden/Load is adaily load, which allows the individual to track their CO exposure.Determining a CO Load is a more accurate reflection of total smokeexposure compared to simply counting cigarettes because smokers smokedifferently. One smoker may smoke the entire cigarette fully and deeplyand intensely, while another smokes less deeply and intensely. Whileboth individuals may smoke one pack per day, the former will have a muchhigher Daily CO Load due to the intensity that the smoke is inhaled. COLoad is also important as when an individual becomes a patient in aquit-smoking program. In such a case, the quantification allows acaregiver or counselor to follow the patient along during as the patientreduces their smoking activity. For example, the patient may reduce from20 cigs per day to 18 to 16 and so on. However, at 10 cigs per day, thepatient may still have a Daily CO Load that has not lowered because theyare compensating when the smoke the reduced number of cigarette (i.e.,the patient smokes harder and deeper and more intensely). The patient'sreduced smoking exposure only occurs when their CO load decreases.

The data shown in FIG. 21 is intended for illustration purposes only andthe duration of the period of time for a given dataset depends on theamount of time the individual uses the portable sampling unit to capturethe biometric and behavioral data. Quantifying the exposure of exhaledcarbon monoxide comprises correlating a function of exhaled carbonmonoxide versus time over the period of time using the dataset andobtaining the area under the eCO curve 412. In variations of the methodand system, the eCO curve can be calculated or approximated..

FIG. 22 illustrates an example of displaying the biometric data as wellas various other information for the benefit of the user, caregiver, orother party having an interest in assessing the smoking behavior of theindividual. The data illustrated in FIG. 22 is for purposes ofillustration and can be displayed on the portable electronic device(e.g., see 110 in FIG. 19) or on one or more computers. In addition, anyof the biometric data or other data can be displayed on the portablesampling unit 1900.

FIG. 22 illustrates a “dashboard” view 118 of the individual's smokingbehavioral data including a graphical output 120 of the eCO curve 412over a period of time as well as the cigarette count for any giveninterval of time within the period of time. Graphical output 120 canalso provide a measured or calculated nicotine trend 424. This nicotinetrend 424 can be determined from the number of cigarettes smoked 426rather than being a direct measurement of nicotine.

FIG. 22 also illustrates a second graphical output display 122 of an eCOcurve 412 over an alternate time period. In this example, the firstgraphical display 120 shows the eCO curve 412 over 7 days while thesecond display 122 shows the data over 3 days. The dashboard view 118can also include additional information including the latest eCOBurden/Load 124 (or the latest eCO reading from the latest sample), thenumber of cigarettes 126 over a defined period such as the current day,as well as the amount of nicotine 128. In addition, the dashboard 118can also include a count of the number of samples 130 provided by theindividual over a defined period (such as a daily through monthlycount).

The dashboard 118 can also display information that can assist theindividual in the reduction or cessation of smoking. For example, FIG.22 also shows a cost of cigarettes 132 using the count of the portion ofcigarettes smoked by the individual 126 or 426. The dashboard can alsodisplay social connections 146, 142, 140 to assist in cessation ofsmoking. For example, the dashboard can display a medical practitioneror counselor 140 that can be directly messaged. In addition, informationcan be displayed on social acquaintances 142 that are also trying toreduce their own smoking behavior.

The dashboard 118 can also display information regarding smokingtriggers 134 as discussed above, for the individual as a reminder toavoid the triggers. The dashboard can also provide the user withadditional behavioral information, including but not limited to theresults of behavioral questionnaires 136 that the individual previouslycompleted with his/her medical practitioner or counselor.

The dashboard 118 can also selectively display any of the informationdiscussed herein based on an analysis of the individual. For example, itmay be possible to characterize the individual's smoking behaviors andassociate such behaviors with certain means that are effective inassisting the individual in reducing or ceasing smoking. In these cases,where the individual's behaviors allow for classifying in one or morephenotypes (where the individual's observable traits allow classifyingwithin one or more groups). The dashboard can display information thatis found to be effective for that particular phenotype. Furthermore, theinformation on the dashboard can be selectively adjusted by the user toallow for customization that the individual finds to be effective as anon-smoking motivator.

FIG. 23 shows another variation of a dashboard 118 displaying similarinformation to that shown in FIG. 22. As noted above, the displayedinformation is customizable. For example, this variation illustrates theeCO load 140 in a graphical display that shows historical data(yesterday's load), current eCO Burden or load, as well as a targetlevel for that of non-smokers. As shown in FIGS. 22 and 23, theindividual previous attempts at quitting smoking 138 can be displayed.In addition, the graphical representation 120 of the eCO trend 412 canbe illustrated with individual eCO readings (of the respective sample)can be displayed with information regarding the smoking times 426 aswell as a graphic showing the time or duration of smoking (as shown bythe circles of varying diameter). As noted above, such information canbe entered by the portable sampling unit and displayed in additionalforms as shown in 126 and 127, which respectively show historical andcurrent data regarding the number of times smoked and the number ofwhole cigarettes smoked.

FIGS. 24A to 24C illustrate another variation of a dataset comprisingexhaled carbon monoxide, collection time, and cigarette data quantifiedand displayed to benefit the individual attempting to understand theirsmoking behavior. FIG. 24A illustrates an example where a patientcollected breath samples over the course of a number of days. Theexample data shown in FIGS. 24A to 24C demonstrate data shown over 21days but any time range is within the scope of the systems and methodsdescribed herein.

As illustrated in FIG. 24A, the period of time 432 is illustrated alongthe horizontal axis with the time intervals being each day within thetime period. Although not shown, during the early stages of samplecollection, the time period itself can comprise one or more days withthe time interval being a multiple of hours or minutes. Clearly, thelonger the time period the greater the ability of the program to selectmeaningful time intervals within the time period.

FIG. 24A illustrates a variation of a dashboard 118 where smoking data(comprising the total number of cigarettes 428 and an associated curve430) are superimposed on a graph showing an eCO curve 412. As notedabove, the individual provides breath samples on a regular or randombasis. In certain variations, the portable sampling unit (not show)prompts the individual to provide samples for measurement of CO. Theportable sampling unit allows the samples to be associated with a timestamp and transmits other user generated data as discussed above. The COdata is then quantified to provide a value for the exposure of CO (eCOfor exhaled CO) over an interval of time (e.g., per day as shown in FIG.24A).

FIG. 24A also demonstrates the ability to show historical datasimultaneously with present data. For example, the CO load data 140illustrates the previous day's CO load as well as the highest COreading, lowest CO reading, and average CO reading. Similar historicalis shown regarding the cigarette data as well as the smoking cessationquestionnaire results 136.

FIGS. 24B and 24C illustrate the dataset in graphical form as theindividual decreases his/her smoking behavior. AS shown in FIG. 24C, asthe individual continues to provide samples for measurement of CO, thegraphical representation of the dataset shows the individual'sself-reporting of smoking fewer cigarettes, which is verified throughthe reduced values of the CO load 124.

The systems and methods described herein, namely quantification anddisplay of smoking behavior as well as other behavioral data provide abase for which healthcare professionals can leverage into effectiveprograms designed to reduce the effects of cigarette smoke. For example,the system and methods described herein can be used to simply identify apopulation of smokers from within a general population. Once thispopulation is identified, building the dataset on the individualsspecific smoking behavior can be performed prior to attempting to enrollthat individual in a smoking cessation program. As noted above, thequantification of the smoking burden (or CO burden) along with the timedata of the smoking activity can be combined with other behavioral datato identify smoking triggers unique to that individual. Accordingly, theindividual's smoking behavior can be well understood by the healthcareprofessional prior to selecting a smoking cessation program. Inaddition, the systems and methods described herein are easily adapted tomonitor an individual's behavior once that individual enters a smokingcessation program and can monitor the individual, once they stopsmoking, to ensure that the smoking cessation program remains effectiveand that the individual refrains from smoking.

In addition, the systems and methods described above regardingquantification of smoking behavior can be used to build, update andimprove the model for smoking behavior discussed above as well as toprovide perturbations to assist in ultimately reducing the individual'ssmoking behavior.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Combination of the aspect of the variations discussed above as wellcombinations of the variations themselves are intended to be within thescope of this disclosure.

Various changes may be made to the invention described and equivalents(whether recited herein or not included for the sake of some brevity)may be substituted without departing from the true spirit and scope ofthe invention. Also, any optional feature of the inventive variationsmay be set forth and claimed independently, or in combination with anyone or more of the features described herein. Accordingly, the inventioncontemplates combinations of various aspects of the embodiments orcombinations of the embodiments themselves, where possible. Reference toa singular item, includes the possibility that there are plural of thesame items present. More specifically, as used herein and in theappended claims, the singular forms “a,” “and,” “said,” and “the”include plural references unless the context clearly dictates otherwise.

What is claimed is:
 1. A method of quantifying an individual's smokingbehavior, the method comprising: obtaining a plurality of samples ofexhaled air from the individual over a period of time and recording acollection time associated with each sample of exhaled air; measuring anamount of exhaled carbon monoxide for each of the samples of exhaledair; compiling a dataset comprising the amount of exhaled carbonmonoxide and the collection time for each sample of exhaled air;quantifying an exposure of exhaled carbon monoxide over an interval oftime within the period of time and assigning an exhaled carbon monoxideload to the interval of time using the dataset; and displaying theexhaled carbon monoxide load.